CN104204798A - Biomarkers for bladder cancer and methods using the same - Google Patents

Abstract

Methods for identifying and evaluating biochemical entities useful as biomarkers for bladder cancer, target identification/validation, and monitoring of drug efficacy are provided. Also provided are suites of small molecule entities as biomarkers for bladder cancer.

Description

Biomarkers for bladder cancer and methods of using the biomarkers

Cross References to Related Applications

This application claims the benefit of U.S. Provisional Patent Application No. 61/558,688, filed November 11, 2011, and U.S. Provisional Patent Application No. 61/692,738, filed August 24, 2012, both of which are incorporated by reference in their entirety In this article.

field of invention

The present invention generally relates to biomarkers for bladder cancer and methods based on said biomarkers.

Background of the invention

In the United States, more than 90% of bladder cancer (BCA) cases are transitional cell carcinoma (TCC), also known as urothelial carcinoma (UC). Approximately 70% of newly diagnosed TCC/UC patients have non-muscle-invasive bladder cancer (NMIBC) tumors (ie, T0a, T1, and CIS). Management of patients with NMIBC includes enucleation of visible tumor by transurethral resection of bladder tumor (TURB-T) and active monitoring for tumor recurrence to minimize the risk of cancer progression.

Cystoscopy is considered the gold standard for diagnosing bladder cancer and monitoring patients with non-muscle invasive bladder cancer (NMIBC). The main limitations of this technique are the inability to image certain areas of the urothelium and the difficulty in imaging carcinoma in situ (CIS) tumors. In both cases, the tumor was not found because of its location in the upper urinary tract or because of its relatively normal appearance on visible light cystoscopy. CIS detection has recently benefited from the introduction of fluorescent dyes injected into the bladder prior to cystoscopy. Although the detection rate has increased, it still requires a longer procedure (incubation of the dye after intravesical injection) and is not used on a routine basis in the United States.

Cytology is usually done, which can help detect bladder tumors that are invisible or poorly visible with cystoscopy. Cytology has been used in routine clinical practice for more than 60 years. However, cytology is a complex method with high inter-operator variability. It is important to note that cytology was not a laboratory test but a consultation; the interpretation of the morphological features of exfoliated urothelial cells was evaluated by individual pathologists. Nonetheless, cytology has a reputation for being extremely specific and sensitive for high-grade tumors (ie, TaG3, T1/G3, and CIS).

However, there is evidence of underperformance of cytology in low-grade tumors (i.e., TaG1/G2) and the notion of high-performance cytology in high-grade tumors has recently been challenged. For example, a Mayo Clinic (n=75) study showed that the overall sensitivity of cytology was 58% for all tumor types, 47% for Ta, only 78% for CIS, and 60% for pT1-pT4. In contrast, fluorescence in situ hybridization (FISH) analysis of the exact same Mayo Clinic sample set had an overall sensitivity of 81%, 65% for Ta, 100% for CIS, and 95% for T1-T4 tumors ( Halling K. et al (2000) A comparison of cytology and fluorescence in situ hybridization for the detection of urothelial carcinoma (comparison of cytology and fluorescence in situ hybridization for the detection of urothelial carcinoma). J. Urol. 164; 1768).

In another example, a different study (n=668) focused on the FDA-approved NMP22 trial as an adjunct to cystoscopy to evaluate recurrence in a series of consecutive patients with a history of bladder cancer at different institutions (Grossman HB et al (2006 ) Surveillance for recurrent bladder cancer using a point-of-care proteomic assay. JAMA 295 ; 299-305). Furthermore, the study highlights that cytology does not perform as well as previously thought in high-grade tumors. Despite better NMP22 sensitivity (49.5%) compared to cytology (12.2%), the positive predictive value (PPV) of both assays was essentially the same at 41.5%, highlighting the significant difference in cytology in terms of specificity. Predominance (99% for cytology and 87% for NMP22). Additionally, several published reviews of studies evaluating the sensitivity/specificity of cytology reaffirm the high specificity of cytology (0.99, with a 95% CI of [0.83-0.997]) and its relatively poor sensitivity of 0.34 (95% CI of [0.20-0.53]) (Lotan Y. and Roehrborn CG (2003) Sensitivity and specificity of commonly available bladder tumor markers versus cytology: results of a comprehensive literature review and meta-analysis (commonly available bladder tumor markers versus cytology sensitivity and specificity: results of a comprehensive literature review and meta-analysis). Urology 61; 109-118).

Nonetheless, cystoscopy with or without urine cytology is the current standard of care for diagnosing bladder cancer in hematuria/dysuria patients and evaluating recurrence in NMIBC patients. However, cytology evaluation can often be inconclusive and does not achieve its intended goal of aiding in the diagnosis of bladder neoplasms. In addition, negative cytology results do not rule out the presence of tumors (especially low stage/low-grade tumors), given the low sensitivity of cytology evaluation. Furthermore, despite its low sensitivity, cytology has become the reference test against which all new tests are compared.

Due to the limitations of cytology and the invasive nature of cystoscopy, biomarkers are still being sought to provide a clinically useful non-invasive tool for the detection of bladder tumors while reducing the costs associated with monitoring NMIBC patients. There is a clinical need for new non-invasive diagnostic tests to aid in cystoscopy and cytology for the initial diagnosis of bladder cancer and to aid in the detection of recurrent bladder cancer tumors in NMIBC patients.

Several FDA-approved urine-based markers such as bladder tumor antigen, ImmunoCyt, nuclear matrix protein-22, and fluorescence in situ hybridization are available for this purpose. None of these tests relied on metabolites or biochemical biomarkers. Many of these tests have good sensitivity but insufficient specificity, which would lead to too many false positive results if used in routine clinical practice. To date, National Comprehensive Cancer Network (NCCN) guidelines do not recommend the use of these trials outside of a protocol setting.

A urine-based test with a specificity comparable to that of cytology and a sensitivity significantly better than that of cytology, when combined with cystoscopy and/or cytology, will simultaneously eliminate false positives by improving the rate of bladder tumor detection The number of outcomes is minimized to significantly impact clinical practice. Such biomarkers can be used to aid in the initial diagnosis of bladder cancer in symptomatic patients without a history of bladder cancer and to aid in the assessment of bladder cancer recurrence. Biomarkers can be used, for example, in urine tests that quantitatively measure a panel of biomarker metabolites whose levels, when used with a specific algorithm, indicate the presence or absence of a cystic tumor in the bladder in a patient and help Initial diagnosis of bladder cancer in a patient population consistent with symptoms of bladder cancer (ie, hematuria/dysuria) and detection of bladder tumor recurrence in a patient population with a history of NMIBC. Additionally, the biomarkers can be used in conjunction with specific algorithms to constitute a diagnostic test indicating tumor grade and stage.

Summary of the invention

In one aspect, the invention provides a method of diagnosing whether a subject has bladder cancer, the method comprising analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein one or more biomarkers are selected from Table 1, 5, 7, 9, 11 and/or 13, and the level of one or more biomarkers in the sample is compared with the bladder cancer of one or more biomarkers Positive and/or bladder cancer negative reference levels are compared to diagnose whether the subject has bladder cancer.

In another aspect, the present invention also provides a method of determining whether a subject is susceptible to developing bladder cancer, the method comprising analyzing a biological sample of the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein The one or more biomarkers are selected from Tables 1, 5, 7, 9, 11 and/or 13; and comparing the level of the one or more biomarkers in the sample with the level of the one or more biomarkers Bladder cancer positive and/or bladder cancer negative reference levels are compared to determine whether the subject is prone to bladder cancer.

In yet another aspect, the present invention provides a method of monitoring the progression/regression of bladder cancer in a subject, the method comprising analyzing a first biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample. Level, wherein one or more biomarkers are selected from Tables 1, 5, 7, 9, 11 and/or 13, and the first sample is obtained from the subject at the first time point; the second sample of the subject is analyzed Two biological samples to determine the level of one or more biomarkers, wherein the second sample is obtained from the subject at a second time point; and the level of the one or more biomarkers in the first sample is compared with the first sample The levels of one or more biomarkers in the two samples are compared to monitor the progression/regression of bladder cancer in the subject.

In yet another aspect, the invention provides a method of distinguishing bladder cancer from other urological cancers (e.g., kidney cancer, prostate cancer), the method comprising analyzing a biological sample from a subject to determine one or more biological factors of bladder cancer in the sample. Levels of markers, wherein one or more biomarkers are selected from Tables 1, 5, 7, 9, 11 and/or 13, and the level of one or more biomarkers in the sample is compared with one or more Bladder cancer positive and/or bladder cancer negative reference levels of various biomarkers were compared to differentiate bladder cancer from other urological cancers.

In another aspect, the invention provides a method of determining whether a subject has recurrent bladder cancer, the method comprising analyzing a biological sample from a subject with a history of bladder cancer to determine an and/or 13 the level of one or more biomarkers of bladder cancer; and the level of one or more biomarkers in the sample is compared with the following: (a) the level of one or more biomarkers A positive reference level for bladder cancer, and/or (b) a negative reference level for bladder cancer of one or more biomarkers.

In another aspect, the present invention also provides a method of determining the stage of bladder cancer, the method comprising analyzing a biological sample of a subject to determine the level of one or more biomarkers for bladder cancer staging in the sample, wherein one One or more biomarkers are selected from Table 5 and/or 9; and the level of one or more biomarkers in the sample is correlated with the high stage bladder cancer and/or low stage of one or more biomarkers The bladder cancer reference level is compared to determine the subject's bladder cancer stage.

In another aspect, the invention provides a method of evaluating the efficacy of a composition for treating bladder cancer, the method comprising analyzing a biological sample from a subject having bladder cancer and currently or previously treated with the composition to determine 1, 5, 7, 9, 11, and/or 13 levels of one or more biomarkers of bladder cancer; and comparing the level of one or more biomarkers in the sample to: (a ) the level of one or more biomarkers in a previously collected biological sample of the subject, wherein the previously collected biological sample was obtained from the subject prior to treatment with the composition, (b) one or more A bladder cancer positive reference level of a biomarker, and/or (c) a bladder cancer negative reference level of one or more biomarkers.

In another aspect, the present invention provides a method for evaluating the efficacy of a composition in the treatment of bladder cancer, the method comprising analyzing a first biological sample from a subject to determine a composition selected from Tables 1, 5, 7, 9, 11 and and/or the level of one or more biomarkers of bladder cancer of 13, the first sample is obtained from the subject at a first time point; the composition is administered to the subject; the second sample of the subject is analyzed a biological sample to determine the level of one or more biomarkers, the second sample is obtained from the subject at a second time point after administration of the composition; the one or more biomarkers in the first sample The level of is compared with the level of one or more biomarkers in the second sample to evaluate the efficacy of the composition for treating bladder cancer.

In yet another aspect, the invention provides a method of evaluating the relative efficacy of two or more compositions for the treatment of bladder cancer, the method comprising analyzing a first patient with bladder cancer currently or previously treated with a first composition. Subject's first biological sample to determine the level of one or more biomarkers selected from Tables 1, 5, 7, 9, 11, and/or 13; analyzed for having bladder cancer and currently or previously using a second a second biological sample from a second subject treated with the composition to determine the level of the one or more biomarkers; and comparing the level of the one or more biomarkers in the first sample to the second sample The levels of one or more biomarkers are compared to assess the relative efficacy of the first and second compositions for treating bladder cancer.

In another aspect, the invention provides methods of screening a composition for activity in modulating one or more biomarkers of bladder cancer, the method comprising contacting one or more cells with the composition; analyzing said one or more At least a portion of one or more cells or a biological sample related to cells to determine the level of one or more biomarkers of bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13; and The level of the one or more biomarkers is compared to a predetermined standard level of the biomarker to determine whether the composition modulates the level of the one or more biomarkers.

In yet another aspect, the present invention provides a method for identifying a potential drug target for bladder cancer, the method comprising identifying one or more of the bladder cancers selected from Tables 1, 5, 7, 9, 11 and/or 13 one or more biochemical pathways associated with the biomarkers; and identifying a protein affecting at least one of the one or more identified biochemical pathways that is a potential drug target for bladder cancer.

In yet another aspect, the present invention provides a method for treating a subject suffering from bladder cancer, the method comprising administering to the subject an effective amount of a compound selected from Tables 1, 5, which is reduced in a subject suffering from bladder cancer. , 7, 9, 11 and/or 13 one or more biomarkers.

Brief description of the drawings

Figure 1 shows the osmolality-normalized abundance ratios of exemplary metabolites between bladder cancer patients (TCC) and case-control subjects.

Figure 2 is a graphical representation of a principal component analysis (PCA) of feature selection for the subjects partitioned using osmolality normalized data in this study. Arbitrary cutoff lines were drawn to account for these metabolic abundance distributions to classify patients into two groups with high negative predictive value (NPV) (PC1 < -1) and high positive predictive value (PPV) (PC1 > 1). Individuals with intermediate values (-1 < PC1 < 1) cannot be classified using this calculation method.

Figure 3 is a graphical representation of the feature-selected hierarchical clustering (Pearson's correlation) of the subjects divided using osmolality normalized values in this study. Three distinct metabolic classes were identified, one with 100% control (no TCC) individuals, one with 100% bladder cancer (TCC) cases, and an intermediate case with 33% controls and 67% TCC cases.

4 is a graph of receiver operating characteristic (ROC) curves using five exemplary biomarkers for bladder cancer as described in Example 7. FIG.

5 is a graph of ROC curves generated as described in Example 7 using seven exemplary biomarkers to differentiate bladder cancer from non-cancer.

6 illustrates a comparison of AUC results obtained using a ridge model using various biomarkers to distinguish BCA from non-cancer as described in Example 7.

7 is a graph of ROC curves generated using ridge logistic regression as described in Example 7 to differentiate bladder cancer from hematuria.

8 illustrates a comparison of AUC results obtained as described in Example 7 using a ridge model to distinguish BCA from hematuria with various biomarkers.

Figure 9 is a diagram of the tricarboxylic acid cycle (TCA) and box plots of biomarker metabolite levels measured in control individuals (left) and bladder cancer patients (right). The y-axis values indicate the scaled intensities of the biomarkers. The top and bottom of the shaded boxes indicate the 75th and 25th percentiles, respectively. The top and bottom lines ("whiskers") represent the full range of data points for each compound and group, excluding "extreme" points, which are represented by circles. "+" indicates the mean value, and the solid line indicates the median value.

Figure 10 is a schematic representation of biochemical pathways and box plots of metabolites indicating glycolytic activity, branched-chain amino acid catabolism, and fatty acid oxidation. Box plots on the left are levels measured in control individuals and box plots on the right are levels measured in bladder cancer (TCC) patients. The y-axis values indicate the scaled intensities of the biomarkers. The top and bottom of the shaded boxes indicate the 75th and 25th percentiles, respectively. The top and bottom lines ("whiskers") represent the entire range of data points for each compound and group, excluding "extreme" points, which are represented by circles. "+" indicates the mean value, and the solid line indicates the median value.

Detailed description of the invention

Currently available FDA-approved tests are based on protein or DNA technology. It is generally accepted that the biochemical composition of urine undergoes significant variation over time both between and within individuals. Such variability would be a hindrance to the examination of the components in terms of their diagnostic capabilities. The findings that many urinary metabolites differentiate subjects with bladder cancer from those without are new, and the fact that some are clearly produced and others are consumed in the urine makes these The need for external normalizers of the data is minimized. Specific metabolites identified in the urine of bladder cancer patients were largely unexpected based on published data on other cancers, especially kidney cancer. Likewise, using a similar approach, new biomarkers have been identified in tissue samples from bladder cancer patients.

The present invention relates to biomarkers for bladder cancer, methods for diagnosing or aiding in the diagnosis of bladder cancer, methods for differentiating bladder cancer from other urological cancers (e.g. prostate cancer, kidney cancer), determining or aiding in determining susceptibility to bladder cancer Methods for monitoring the progression/regression of bladder cancer, methods for determining recurrence of bladder cancer, methods for staging bladder cancer, methods for evaluating the efficacy of compositions for treating bladder cancer, methods for regulating biomarkers in bladder cancer Methods of screening compositions for activity in, methods of identifying potential drug targets for bladder cancer, methods of treating bladder cancer, and other methods based on biomarkers of bladder cancer. However, before describing the present invention in further detail, the following terms are first defined.

definition:

"Biomarker" means a biomarker that is differentially present (i.e. increased or decreased) in a biomarker having a first phenotype compared to a biological sample of a subject or group of subjects having a second phenotype (e.g., not having a disease). A compound, preferably a metabolite, in a biological sample of a subject or group of subjects (eg suffering from said disease). Biomarkers can be differentially present at any level, but are generally present at levels that increase by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100% , at least 110%, at least 120%, at least 130%, at least 140%, at least 150% or higher; or generally present at a level that reduces by at least 5%, at least 10%, at least 15%, at least 20%, At least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85% %, at least 90%, at least 95%, or 100% (i.e. not present). Biomarkers are preferably present differentially at a statistically significant level (ie, a p-value of less than 0.05 and/or a q-value of less than 0.10 as determined using the Welch T-test or the Wilcoxon rank sum test).

A "level" of one or more biomarkers means the absolute or relative amount or concentration of a biomarker in a sample.

"Sample" or "biological sample" means biological material isolated from a subject. A biological sample may contain any biological material suitable for detection of a desired biomarker, and may comprise cellular and/or non-cellular material of the subject. Samples may be isolated from any suitable biological tissue or fluid, such as bladder tissue, blood, plasma, urine or cerebrospinal fluid (CSF).

"Subject" means any animal, but preferably a mammal, such as a human, monkey, mouse, rabbit or rat.

A "reference level" of a biomarker means the level of a biomarker indicative of a particular disease state, phenotype or lack thereof and combinations of disease states, phenotypes or lack thereof. A "positive" reference level of a biomarker means a level indicative of a particular disease state or phenotype. A "negative" reference level of a biomarker means a level indicative of the absence of a particular disease state or phenotype. For example, a "positive reference level for bladder cancer" of a biomarker means a level of a biomarker that indicates a positive diagnosis of bladder cancer in a subject, and a "negative reference level for bladder cancer" of a biomarker means a level that indicates a positive diagnosis of bladder cancer in a subject. Biomarker levels for negative diagnosis of cancer. A "reference level" for a biomarker can be an absolute or relative amount or concentration of a biomarker, the presence or absence of a biomarker, a range of amounts or concentrations of a biomarker, a minimum and/or Maximum amount or concentration, mean amount or concentration of a biomarker, and/or median amount or concentration of a biomarker; alternatively, a "reference level" for a combination of biomarkers can be two or more Absolute or relative amounts or concentration ratios of biomarkers to one another. Suitable positive and negative reference levels of biomarkers for a particular disease state, phenotype, or lack thereof can be determined by measuring the level of the desired biomarker in one or more suitable subjects, and the The reference level is adapted to a particular population of subjects (e.g., the reference level can be age-matched so that the biomarker level in a sample from a subject of a certain age can be correlated with a particular disease state, phenotype, comparison between reference levels or lack thereof). The reference level can also be adapted to the particular technique (e.g., LC-MS, GC-MS, etc.) used to measure the level of the biomarker in the biological sample, where the level of the biomarker can vary depending on the particular technique employed. different.

"Non-biomarker compound" means a compound having a first phenotype that is not differentially present in a biological sample of a subject or group of subjects having the second phenotype (e.g., not having the first disease). A compound in a biological sample of a subject or group of subjects (eg, suffering from the first disease). However, the non-biomarker compound may be of a third phenotype (e.g., having the first disease) or a second phenotype (e.g., not having the first disease) compared to the first phenotype (e.g., having the first disease) A biomarker in a biological sample of a subject or group of subjects with a second disease).

"Metabolite" or "small molecule" means organic and inorganic molecules present in cells. The term does not include large macromolecules, such as large proteins (e.g., proteins with molecular weights exceeding , 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 nucleic acids) or large polysaccharides (such as polysaccharides with a molecular weight exceeding 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000). A cell's small molecules generally exist free in solution in the cytoplasm or other organelles (eg, mitochondria), where they form a pool of intermediates that can be further metabolized or used to produce larger molecules, called macromolecules. The term "small molecule" includes signal transduction molecules and intermediates of chemical reactions that convert energy from food into a usable form. Examples of small molecules include sugars, fatty acids, amino acids, nucleotides, intermediates formed in cellular processes, and other small molecules present within cells.

"Metabolic profile" or "small molecule profile" means the total or partial inventory of small molecules within a target cell, tissue, organ, organism, or part thereof (eg, a cellular compartment). The inventory can include the amount and/or type of small molecules present. A "small molecule profile" can be determined using a single technique or a plurality of different techniques.

"Metabolome" means the totality of small molecules present in a given organism.

"Bladder cancer" (BCA) or "transitional cell carcinoma" (TCC) refers to a disease in which the cancer develops in the bladder. As used herein, both BCA and TCC are used interchangeably to denote bladder cancer.

The "stage" of bladder cancer is an indication of how far the tumor has spread. Tumor staging is used to select treatment options and to estimate a patient's prognosis. Bladder tumor stages range from T0 (no evidence of a primary tumor, the earliest stage) to T4 (the tumor has spread beyond the fatty tissue surrounding the bladder into nearby organs, the latest stage). Early stages of bladder cancer can also be characterized as carcinoma in situ (CIS), meaning that cells have proliferated abnormally but are still contained within the bladder. "Low-stage" or "lower-stage" bladder cancer refers to bladder cancer neoplasms, including malignancies that have a lower potential for recurrence, progression, invasion, and/or metastasis (ie, bladder cancers that are considered less aggressive). Cancerous tumors confined to the bladder (ie, non-muscle invasive bladder cancer, NMIBC) are considered less aggressive bladder cancers. "High stage" or "higher stage" bladder cancer refers to bladder cancer tumors that are more likely to recur and/or progress and/or become invasive in a subject, including malignancies with higher metastatic potential (considered more aggressive bladder cancer). Cancerous tumors that are not limited to the bladder (ie, muscle-invasive bladder cancer) are considered more aggressive bladder cancers.

A "history of bladder cancer" refers to a patient who has previously had bladder cancer.

"Prostate cancer" (PCA) refers to a disease in which cancer occurs in the prostate gland.

"Kidney cancer" or "renal cell carcinoma" (RCC) refers to a disease in which the cancer develops in the kidney.

"Urological cancer" (UCA) refers to diseases in which cancer occurs in the bladder, kidney and/or prostate.

"Hematuria" refers to a condition in which blood is present in the urine.

"Cytology" refers to an FDA-approved procedure that is part of the standard of care and is used in conjunction with or as a reflection of cystoscopy to detect bladder cancer recurrence or diagnosis. It identifies tumor cells based on morphological properties. It is not a test per se but a pathological consultation based on urine samples. The procedure is complex and requires special skill and care in sample collection to provide correct evaluation. Historically, the performance of cytology has been described as excellent for high-grade tumors, but recent research challenges this perception. On the other hand, all studies were generally consistent with regard to low cytological sensitivity in low-grade low-stage tumors (the majority of NMIBC tumors). Its two main strengths are its long history of use in clinical practice (deep entrenched) and its very high specificity (evaluated between 90 and 100%, with many studies setting it at 99%). This provides a very positive predictive value for cytology diagnosis. This method is the method against which all other assays are currently evaluated, either as a substitute or as an adjunct to cytological evaluation.

A "BCA score" is a measure or indicator of bladder cancer severity based on the bladder cancer biomarkers and algorithms described herein. The BCA score enables physicians to place patients on a scale of bladder cancer severity from normal (ie, no bladder cancer) to high (eg, high stage or more aggressive bladder cancer). Those of ordinary skill in the art will appreciate that the BCA score can have various uses in the diagnosis and treatment of bladder cancer. For example, the BCA score can also be used to distinguish low-stage bladder cancer from high-stage bladder cancer, and to monitor the progression and/or regression of bladder cancer.

I. Biomarkers

Using metabolomic profiling techniques, the bladder cancer biomarkers described herein were discovered.所述代谢物组概况分析技术的更多详情描述于下文的实施例以及美国专利号7,005,255、7,329,489、7,550,258、7,550,260、7,553,616、7,635,556、7,682,783、7,682,784、7,910,301、6,947,453、7,433,787、7,561,975、7,884,318，所述The entire content of the patent is incorporated herein by reference.

The metabolic profile is generally determined on a biological sample from a human subject who is positive for bladder cancer or a sample from a human subject who is negative for bladder cancer (control case). Exemplary controls include cancer negative healthy subjects, cancer negative hematuria subjects, bladder cancer negative cancer subjects. The metabolic profile of a biological sample from a subject with bladder cancer is compared to the metabolic profile of biological samples from one or more other groups of subjects. Those molecules that are differentially present in the metabolic profile of bladder cancer-positive samples compared to another group (e.g., bladder cancer-negative samples), including those molecules that are differentially present at a statistically significant level, are identified as distinguishing these groups of biomarkers.

This article discusses biomarkers in more detail. The biomarkers found were consistent with biomarkers distinguishing subjects with bladder cancer from control subjects not diagnosed with bladder cancer (see Tables 1, 5, 7, 9, 11 and/or 13).

The metabolic profile of a biological sample from a human subject diagnosed with high-stage bladder cancer or a human subject diagnosed with low-stage bladder cancer was also determined. The metabolic profile of a biological sample from a subject with high-stage bladder cancer is compared to the metabolic profile of a biological sample from a subject with low-stage bladder cancer. Those small molecules that are differentially present in the metabolic profile of samples from subjects with high-stage bladder cancer compared to another group (e.g., subjects not diagnosed with high-stage bladder cancer), including statistically Those small molecules that were differentially present at a significant level were identified as biomarkers that differentiated these groups.

This article discusses biomarkers in more detail. The biomarkers found were consistent with biomarkers distinguishing subjects with high-stage bladder cancer from those with low-stage bladder cancer (see Tables 5 and 9).

II. Method

A. Diagnosis of Bladder Cancer

Identification of biomarkers for bladder cancer allows the diagnosis (or ancillary diagnosis) of bladder cancer in subjects presenting with one or more symptoms consistent with the presence of bladder cancer, including subjects not previously identified as having bladder cancer Primary diagnosis of bladder cancer in subjects and diagnosis of bladder cancer recurrence in subjects who had previously been treated for bladder cancer. The method for diagnosing (or assisting in diagnosing) whether a subject has bladder cancer comprises: (1) analyzing a biological sample of the subject to determine the level of one or more biomarkers of bladder cancer in the sample, and (2) The level of one or more biomarkers in the sample is compared with the bladder cancer positive and/or bladder cancer negative reference levels of one or more biomarkers to diagnose (or assist in diagnosis) whether the subject has have bladder cancer. The one or more biomarkers used are selected from Tables 1, 5, 7, 9, 11 and/or 13 and combinations thereof. When the method is employed to aid in the diagnosis of bladder cancer, the results of the method can be used in conjunction with other methods (or results thereof) that are clinically useful in determining whether a subject has bladder cancer.

Any suitable method can be used to analyze a biological sample to determine the level of one or more biomarkers in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, and combination. Additionally, the level of one or more biomarkers can be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker desired to be measured.

The level of one or more biomarkers in Table 1, 5, 7, 9, 11 and/or 13 can be determined in the diagnostic method and auxiliary diagnostic method of whether the subject has bladder cancer. For example, one or more of the following biomarkers can be used alone or in combination to diagnose or aid in the diagnosis of bladder cancer: lactate, palmitoyl sphingomyelin, phosphorylcholine, succinate, adenosine, 1, 2-Propanediol, adipate, anserine, 3-hydroxybutyric acid (BHBA), pyridoxate, acetylcarnitine, 2-hydroxybutyric acid (AHB), kynurenine, tyramine , Adenosine 5'-monophosphate (AMP), 3-hydroxyphenylacetic acid (3-hydroxyphenylacetate), 2-hydroxyhippuric acid (salicyluric acid), 3-hydroxyindoxyl-sulfate (3-indoxyl-sulfate), benzene Acetylglutamine, p-cresol-sulfate, 3-hydroxyhippuric acid, itaconate, methylenesuccinate, cortisol, isobutyrylglycine, gluconate ), xanthurenate, guluronic acid 1,4-lactone, cinnamoyl glycine, 2-oxindole-3-acetic acid (2-oxindole-3-acetate), α-CEHC-glucuronide, Catechol-sulfate, γ-glutamylphenylalanine, 2-isopropylmalate, 4-hydroxyphenylacetate, isovalerylglycine, meat Alkali, tartarate, 6-phosphogluconate, stearoyl sphingomyelin, inositol, glucose, 3-(4-hydroxyphenyl)lactate , 1-linoleoylglycerol (1-monolinolein), pro-hydroxy-pro, γ-glutamylglutamate (gamma-glutamylglutamate), creatine, 5,6-dihydrouracil, twenty Docosadienoate (22:2n6), phenyllactate (PLA), propionylcarnitine, isoleucylproline, N2-methylguanosine, eicosapentaenoic acid (eicosapentanenoate) (EPA 20:5n3), 5-methylthioadenosine (MTA), α-glutamyl lysine, 3-phosphoglycerate (3-phosphoglycerate), 6-keto prostaglandin F1α, twenty-two Docostrienoate (22:3n3), 2-palmitoleyl glycerophosphocholine, 1-stearyl glycerophosphoinositol, 1-palmitoyl glycerophosphoinositol, scyllo-inositol, double high- Linoleic acid (dihomo-linolea te) (20:2n6), 3-phosphoserine, docosapentaenoate (n6 DPA 22:5n6), 1-palmitoylglycerol and (1-glycerol monopalmitate). Additionally, one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers can be determined, for example , six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers etc. (including combinations of all biomarkers in Tables 1, 5, 7, 9, 11 and/or 13 and any fraction thereof) and used in the method. Determining the levels of combinations of biomarkers may allow for greater sensitivity and specificity in diagnosing and aiding in the diagnosis of bladder cancer. For example, ratios of the levels of certain biomarkers (and non-biomarker compounds) in biological samples may allow for greater sensitivity and specificity in diagnosing and aiding in the diagnosis of bladder cancer.

One or more biomarkers specific for diagnosing bladder cancer (or to aid in diagnosing bladder cancer) in a certain type of sample (eg, urine sample or tissue plasma sample) can also be used. For example, when the biological sample is urine, one or more biomarkers listed in Tables 1, 5, 11 and/or 13 or any combination thereof can be used to diagnose (or assist in the diagnosis) whether the subject has Bladder Cancer. When the sample is bladder tissue, one or more biomarkers selected from Table 7 and/or 9 can be used to diagnose (or assist in diagnosis) whether the subject has bladder cancer.

After determining the level of one or more biomarkers in the sample, the level is compared with bladder cancer positive and/or bladder cancer negative reference levels to assist in diagnosis or to diagnose whether the subject has bladder cancer. The level of one or more biomarkers in the sample that matches a positive reference level for bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level, and/or or the maximum value and/or a level within the range of the reference level) indicates that the subject is diagnosed with bladder cancer. The level of one or more biomarkers in the sample that matches the negative reference level for bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level, and/or or the maximum value and/or a level within the range of the reference level) indicates that the subject is diagnosed as having no bladder cancer. Additionally, the presence of a level of one or more biomarkers that is differentially present in the sample, particularly at a level of statistical significance, compared to a negative reference level for bladder cancer indicates a diagnosis of bladder cancer in the subject. A differential presence, especially at a level of statistical significance, of the level of one or more biomarkers in the sample compared to a bladder cancer positive reference level indicates a diagnosis of no bladder cancer in the subject.

The level of one or more biomarkers can be compared to bladder cancer positive and/or bladder cancer negative reference levels using various techniques, including comparing the level of one or more biomarkers in a biological sample with Simple comparison (e.g. manual comparison) of bladder cancer positive and/or bladder cancer negative reference levels. One or more statistical analyzes (e.g. t test, Welch T test, Wilcoxon rank sum test, random forest (Random forest), T score, Z score) or applied mathematical models (e.g. algorithm, statistical model) can also be applied, The level of one or more biomarkers in the biological sample is compared to bladder cancer positive and/or bladder cancer negative reference levels.

For example, a mathematical model comprising a single algorithm or multiple algorithms can be used to determine whether a subject has bladder cancer. Mathematical models can also be used to classify bladder cancer stages. Exemplary mathematical models can utilize any number of biomarkers (e.g., 2, 3, 5, 7, 9 species, etc.) to determine whether the subject has bladder cancer, whether the subject's bladder cancer has progressed or regressed, whether the subject has high-stage or low-stage bladder cancer, and the like.

The results of this method can be used with other methods (or results thereof) that can be used to diagnose bladder cancer in a subject.

In one aspect, the biomarkers provided herein can be used to provide a physician with a BCA score that indicates the presence and/or severity of bladder cancer in a subject. Scores are based on reference levels of clinically significant changes in biomarkers and/or combinations of biomarkers. A reference level can be derived from an algorithm. The BCA score can be used to place subjects on a scale of bladder cancer severity from normal (ie, no bladder cancer) to high. The BCA score can be used in a variety of ways: for example, disease progression, regression, or remission can be monitored by periodic measurement and monitoring of the BCA score; response to therapeutic intervention can be determined by monitoring the BCA score; drug efficacy can be assessed using the BCA score.

The method of determining a subject's BCA score can be performed using one or more of the bladder cancer biomarkers identified in Tables 1, 5, 7, 9, 11 and/or 13 in a biological sample. The method can include comparing the level of the one or more bladder cancer biomarkers in the sample to a bladder cancer reference level of the one or more biomarkers to determine the subject's BCA score. The method may utilize any number of markers selected from those listed in Tables 1, 5, 7, 9, 11 and/or 13, including 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more markers. Various biomarkers can be correlated with bladder cancer by any method, including statistical methods such as regression analysis.

After determining the level of one or more biomarkers, the levels can be compared to bladder cancer reference levels or a reference curve for one or more biomarkers to determine the presence of one or more biological markers in the sample. Ratings for each marker. Any algorithm may be used to sum the ratings to obtain a subject's score, eg, a BCA score. The algorithm can take into account any factors related to bladder cancer including the number of biomarkers, correlation of biomarkers to bladder cancer, and the like.

Additionally, in one embodiment, the biomarkers provided herein for diagnosing or aiding in the diagnosis of bladder cancer can be used to differentiate bladder cancer from hematuria in a subject with hematuria. The method of distinguishing bladder cancer from hematuria in a subject comprises (1) analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, and (2) treating one of the samples with The level of one or more biomarkers is compared to bladder cancer positive and/or bladder cancer negative reference levels of one or more biomarkers to differentiate bladder cancer from hematuria. The one or more biomarkers used are selected from Tables 1, 5, 7, 9, 11 and/or 13. For example, one or more of the following biomarkers can be used alone or in any combination to differentiate bladder cancer from hematuria: xanthuric acid, isovalerylglycine, 2-hydroxybutyrate (AHB), 4-hydroxyhippuric acid, glucose acid, gulonic acid 1,4-lactone, 3-hydroxyhippuric acid, tartaric acid, 2-oxindole-3-acetic acid, isobutyryl glycine, catechol sulfate, phenylacetyl glutamine, succinic acid, 3-Hydroxybutyric acid (BHBA), cinnamoylglycine, isobutyrylcarnitine, 3-hydroxyphenylacetic acid, 3-hydroxyindole sulfate, sorbose, 2-5-furandicarboxylic acid, methyl-4-hydroxybenzene Formic acid (methyl-4-hydroxybenzoate), 2-isopropylmalic acid, adenosine 5'-monophosphate (AMP), 2-methylbutyrylglycine, palmitoylsphingomyelin, phenylpropionylglycine, β-hydroxy Pyruvate, tyramine, 3-methylcrotonylglycine, carnosine, fructose, lactic acid, phosphorylcholine, adenosine, 1,2-propanediol, adipic acid, anserine, pyridoxic acid, acetylcarnitine, and kynurine acid. When the method is used to differentiate bladder cancer from hematuria, the results of the method can be used with other methods (or results thereof) of clinical assays that can be used to differentiate bladder cancer from hematuria.

In another embodiment, the biomarkers provided herein for diagnosing or aiding in the diagnosis of bladder cancer can be used to differentiate bladder cancer from other urological cancers. The method of distinguishing bladder cancer from other urological cancers in a subject comprises (1) analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, and (2) dividing the sample into The level of one or more biomarkers is compared with bladder cancer positive and/or bladder cancer negative reference levels of one or more biomarkers to distinguish bladder cancer from other urological cancers. The one or more biomarkers used are selected from Table 1 and/or Table 11. For example, one or more of the following biomarkers can be used alone or in any combination to distinguish bladder cancer from other urological cancers: imidazole-propionate, 3-hydroxyindolesulfate, phenylacetylglycine , lactic acid, choline, methyl-indole-3-acetic acid, β-alanine, palmitoyl sphingomyelin, 2-hydroxyisobutyric acid, succinic acid, 4-androstene-3β-17β-diol- Disulfate-2 (4-androsten-3beta-17beta-diol-disulfate-2), 4-hydroxyphenylacetic acid, glycerin, uracil, gulonic acid 1,4-lactone, phenol sulfate, dimethylarginine Acids (ADMA + SDMA), Cyclic-gly-pro, Sucrose, Adenosine, Serine, Azelaic acid (nonanedioic acid), Threonine, Pregnanediol-3-glucuronide, Ethanolamine, Gluconic acid, N6 -Methyladenosine, N-methyl-proline, glycine and glucose 6-phosphate (G6P), phosphorylcholine, 1,2-propanediol, adipic acid, anserine, 3-hydroxybutyric acid (BHBA) , pyridoxic acid, acetylcarnitine, 2-hydroxybutyric acid, kynurenine, tyramine and xanthuric acid. When the method is used to distinguish bladder cancer from other urological cancers, the results of the method can be used with other methods (or results thereof) of clinical assays that can be used to distinguish bladder cancer from other urological cancers.

B. Methods for Determining Susceptibility to Bladder Cancer

Identification of biomarkers for bladder cancer also allows for the determination of whether a subject without symptoms of bladder cancer is susceptible to developing bladder cancer. The method of determining whether a subject without symptoms of bladder cancer is susceptible to bladder cancer comprises (1) analyzing a biological sample from the subject to determine one of the following in the sample, listed in Tables 1, 5, 7, 9, 11, and/or 13. the level of one or more biomarkers, and (2) comparing the level of one or more biomarkers in the sample to the bladder cancer positive and/or bladder cancer negative reference level of one or more biomarkers A comparison is made to determine whether a subject is prone to developing bladder cancer. The results of the methods can be used with other methods (or results thereof) that are clinically useful in determining whether a subject is susceptible to developing bladder cancer.

As described above in conjunction with methods of diagnosing (or assisting in diagnosing) bladder cancer, any suitable method may be used to analyze a biological sample to determine the level of one or more biomarkers in the sample.

In terms of the above method for diagnosing (or assisting in diagnosing) bladder cancer, one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers can be determined markers, five or more biomarkers, six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers markers, ten or more biomarkers, etc. (including combinations of all biomarkers in Tables 1, 5, 7, 9, 11 and/or 13 or any part thereof), and used to determine the absence of bladder cancer A method for determining whether symptomatic subjects are susceptible to bladder cancer.

After determining the level of one or more biomarkers in the sample, the level is compared to bladder cancer positive and/or bladder cancer negative reference levels to predict whether the subject is prone to bladder cancer. The level of one or more biomarkers in the sample that matches a positive reference level for bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level, and/or or a maximum value and/or a level within the range of the reference level) indicates that the subject is susceptible to bladder cancer. The level of one or more biomarkers in the sample that matches the negative reference level for bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level, and/or or the maximum value and/or a level within the range of the reference level) indicates that the subject is not prone to bladder cancer. In addition, the presence of a level of one or more biomarkers that is differentially present in the sample, especially at a level of statistical significance, compared to a negative reference level for bladder cancer indicates that the subject is susceptible to developing bladder cancer. A level of one or more biomarkers that is differentially present in the sample, especially at a level of statistical significance, compared to a positive reference level for bladder cancer indicates that the subject is less prone to developing bladder cancer.

In addition, a reference level specific for assessing the susceptibility of bladder cancer to a subject without bladder cancer can be determined. For example, reference levels of biomarkers for assessing different degrees of risk (eg, low, intermediate, high) of developing bladder cancer in a subject can be determined. The reference level can be used for comparison to the level of one or more biomarkers in a subject's biological sample.

With respect to the methods described above, various techniques can be used, including simple comparison, one or more statistical analyzes and combinations thereof, to compare the level of one or more biomarkers to bladder cancer positive and/or bladder cancer negative reference level for comparison.

For methods of diagnosing (or assisting in diagnosing) whether a subject has bladder cancer, the method of determining whether a subject without symptoms of bladder cancer is susceptible to developing bladder cancer may also include analyzing a biological sample to determine one or more non- Levels of biomarker compounds.

C. Methods for Monitoring Progression/Regression of Bladder Cancer

Identification of biomarkers for bladder cancer also allows monitoring the progression/regression of bladder cancer in a subject. The method of monitoring the progression/regression of bladder cancer in a subject comprises (1) analyzing a first biological sample of the subject to determine one of the bladder cancers selected from Tables 1, 5, 7, 9, 11 and/or 13 or more biomarkers, the first sample is obtained from the subject at a first time point, (2) analyzing a second biological sample from the subject to determine the one or more biomarkers , the second sample is obtained from the subject at a second time point, and (3) comparing the level of one or more biomarkers in the first sample with the level of one or more biological markers in the second sample The levels of the markers were compared to monitor the progression/regression of the subject's bladder cancer. For example, one or more of the following biomarkers can be used alone and in combination to monitor progression/regression of bladder cancer: 3-hydroxyphenylacetic acid, 3-hydroxyhippuric acid, 3-hydroxybutyric acid (BHBA), isovaleryl Glycine, phenylacetylglutamine, pyridoxic acid, 2-5-furandicarboxylic acid, allantoin, pimelic acid (heptanedioic acid), lactic acid, adenosine 5'-monophosphate (AMP), catechol Sulfuric Acid, 2-Hydroxybutyric Acid (AHB), Isobutyryl Glycine, 2-Hydroxyhippuric Acid (Salicyluric Acid), Gluconic Acid, Imidazole-Propionic Acid, Succinic Acid, Alpha-CEHC-Glucuronic Acid, 3-Hydroxy Indoxyl sulfate, 4-hydroxyphenylacetic acid, acetylcarnitine, xanthine, p-cresol sulfate, tartaric acid, 4-hydroxyhippuric acid, 2-isopropylmalic acid, palmitoyl sphingomyelin, adipic acid, and N(2 )-furoyl-glycine, phosphorylcholine, adenosine, 1,2-propanediol, anserine, tyramine, xanthuric acid, and kynurenine. The results of the method indicate the progression (ie, progression or regression, if any) of the subject's bladder cancer.

A change in the level of one or more biomarkers over time, if any, can indicate progression or regression of bladder cancer in the subject. To characterize the progression of bladder cancer in a subject, the level of one or more biomarkers in a first sample, the level of one or more biomarkers in a second sample, and/or the first and second The results of the comparison of the biomarker levels in the two samples are compared to bladder cancer positive and bladder cancer negative reference levels. If the comparison shows that the level of one or more biomarkers increases or decreases over time (e.g., in a second sample compared to the first sample) to become more similar to the bladder cancer positive reference level (or less Similar to the bladder cancer negative reference level), the result indicates bladder cancer progression. If the comparison shows that the level of one or more biomarkers increases or decreases over time to become more similar to the bladder cancer negative reference level (or less similar to the bladder cancer positive reference level), the result indicates regression of the bladder cancer .

In one embodiment, the evaluation can be based on a BCA score that is indicative of bladder cancer in the subject and can be monitored over time. Progression or regression of the bladder cancer can be determined by comparing the BCA score of the sample from the first time point to the BCA score of the sample from at least a second time point. Such methods of monitoring the progression/regression of bladder cancer in a subject comprise (1) analyzing a first biological sample of the subject to determine a BCA score of said first sample obtained from the subject at a first time point , (2) analyzing a second biological sample from the subject to determine a second BCA score, the second sample being obtained from the subject at a second time point, and (3) comparing the BCA score of the first sample to the second BCA score The BCA scores of the two samples were compared to monitor the progression/regression of the subject's bladder cancer.

The biomarkers and algorithms described herein can guide or assist physicians in deciding on treatment paths, such as whether to perform procedures such as surgical procedures (e.g., transurethral resection, radical cystectomy, segmental cystectomy), whether to use drugs, etc. Therapeutic treatment or whether to use a watch-and-wait approach.

As with the other methods described herein, the comparisons made in the method of monitoring the progression/regression of bladder cancer in a subject can be made using various techniques, including simple comparisons, one or more statistical analyses, mathematical models ( algorithms) and their combinations.

The results of this method can be used with other methods (or their results) that are useful for clinical monitoring of the progression/regression of bladder cancer in a subject.

As described above in conjunction with methods of diagnosing (or assisting in diagnosing) bladder cancer, any suitable method may be used to analyze a biological sample to determine the level of one or more biomarkers in the sample. Additionally, the level of one or more biomarkers (including combinations of all biomarkers in Tables 1, 5, 7, 9, 11 and/or 13 or any fraction thereof) can be determined and used to monitor the subject Progression/Regression Methods of Bladder Cancer.

The method can be performed to monitor the progression of bladder cancer in a subject with bladder cancer, or the method can be used in a subject without bladder cancer (e.g., a subject suspected of being prone to bladder cancer) to monitor the progression of bladder cancer Susceptibility level to bladder cancer.

D. Methods for Staging Bladder Cancer

Identification of biomarkers for bladder cancer also allows for the determination of the subject's bladder cancer stage. The method for determining the stage of bladder cancer comprises (1) analyzing a biological sample of a subject to determine the level of one or more biomarkers listed in Table 5 and/or Table 9 in the sample, and (2) dividing the sample into The level of the one or more biomarkers is compared with the high-stage bladder cancer and/or low-stage bladder cancer reference level of the one or more biomarkers to determine the stage of the subject's bladder cancer. The results of the method can be used with other methods (or results thereof) that can be used in the clinical determination of a subject's bladder cancer stage.

As described above in conjunction with methods of diagnosing (or assisting in diagnosing) bladder cancer, any suitable method may be used to analyze a biological sample to determine the level of one or more biomarkers in the sample.

The level of one or more biomarkers listed in Table 5 and Table 9, and combinations thereof, can be determined in the method of determining the stage of bladder cancer in a subject. For example, one or more of the following biomarkers can be used alone or in combination to determine the stage of bladder cancer: palmitoyl ethanolamide, palmitoyl sphingomyelin, thromboxane B2, bilirubin (Z, Z), adrenaline ( adrenate) (22:4n6), C-glycosyl tryptophan, methyl-α-glucopyranoside, methyl phosphate, 3-hydroxydecanoic acid, 3-hydroxyoctanoic acid, 4-hydroxyphenylpyruvate, N- Acetyl threonine, 1-arachidonoylglycerophosphoinositol, 5,6-dihydrothymine, 2-hydroxypalmitic acid, coenzyme A, N-acetylserione, nicotinamide gland Purine dinucleotide (NAD+), docosatrienoic acid (22:3n3), reduced glutathione (GSH), prostaglandin A2, glutamine, glutamic acid γ-methyl ester, di Eicosapentaenoic acid (n6 DPA 22:5n6), glycochenodeoxycholic acid, caproylcarnitine, arachidonic acid (20:4n6), pro-hydroxy-pro, docosahexaenoic acid ( DHA 22:6n3), laurylcarnitine, lactic acid, phosphorylcholine, succinic acid, adenosine, 1,2-propanediol, adipic acid, anserine, 3-hydroxybutyric acid (BHBA), pyridoxic acid, acetyl Carnitine, 2-hydroxybutyric acid (AHB), kynurenine, tyramine and xanthuric acid. Additionally, one biomarker, two or more biomarkers, three or more biomarkers, four or more biomarkers, five or more biomarkers can be determined, for example , six or more biomarkers, seven or more biomarkers, eight or more biomarkers, nine or more biomarkers, ten or more biomarkers etc. (comprising the combination of all biomarkers listed in Table 5 and/or Table 9 or any part thereof), and can be used to determine the method for the bladder cancer stage of the subject.

After determining the level of one or more biomarkers in the sample, the level is compared to low-stage bladder cancer and/or high-stage bladder cancer reference levels to determine the subject's bladder cancer stage. The level of one or more biomarkers in the sample that matches a reference level for high-stage bladder cancer (e.g., a minimum value that is the same as the reference level, substantially the same as the reference level, above and/or below the reference level, and /or a maximum value and/or a level within the range of the reference level) indicates that the subject has high-stage bladder cancer. The level of one or more biomarkers in the sample that matches the reference level of low-stage bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level and /or a maximum value and/or a level within the range of the reference level) indicates that the subject has low-stage bladder cancer. Additionally, the presence of a level of one or more biomarkers that is differentially present in the sample, especially at a level of statistical significance, compared to a reference level of low-stage bladder cancer indicates that the subject does not have low-stage bladder cancer. A level of one or more biomarkers that is differentially present, especially at a level of statistical significance, in the sample compared to a reference level of high-stage bladder cancer indicates that the subject does not have high-stage bladder cancer.

A study was conducted to identify a set of biomarkers that could be used to determine the stage of bladder cancer in subjects. In another embodiment, the biomarkers provided herein can be used to provide a physician with a BCA score indicative of the stage of bladder cancer in a subject. Scores are based on reference levels of clinically significant changes in biomarkers and/or combinations of biomarkers. A reference level can be derived from an algorithm. The BCA score can be used to determine the stage of bladder cancer in subjects ranging from normal (ie, no bladder cancer) to high-stage bladder cancer.

The biomarkers and algorithms described herein can guide or assist physicians in deciding on treatment paths, such as whether to perform procedures such as surgical procedures (e.g., transurethral resection, radical cystectomy, segmental cystectomy), whether to use drugs, etc. Therapeutic treatment or whether to use a watch-and-wait approach.

With respect to the methods described above, various techniques can be used, including simple comparisons, one or more statistical analyses, mathematical models (algorithms), and combinations thereof to correlate the levels of one or more biomarkers with high-stage bladder cancer and and/or low-stage bladder cancer reference levels for comparison.

For methods of diagnosing (or assisting in diagnosing) whether a subject has bladder cancer, the method of determining the stage of bladder cancer in a subject may also include analyzing a biological sample to determine the level of one or more non-biomarker compounds .

E. Methods of Evaluating the Efficacy of Compositions for Treating Bladder Cancer

Identification of biomarkers for bladder cancer also allows for evaluation of the efficacy of a composition for treating bladder cancer and for evaluating the relative efficacy of two or more compositions for treating bladder cancer. Such evaluations can be used, for example, in efficacy studies and in the lead selection of compositions for the treatment of bladder cancer.

The method of evaluating the efficacy of a composition for treating bladder cancer comprises (1) analyzing a biological sample from a subject having bladder cancer and currently or previously treated with the composition to determine a group selected from Tables 1, 5, 7, 9, The level of one or more biomarkers of 11 and/or 13, and (2) comparing the level of one or more biomarkers in the sample to: (a) the subject's previously collected The level of one or more biomarkers in a biological sample, wherein said previously collected biological sample was obtained from a subject prior to treatment with the composition, (b) bladder cancer positive reference for one or more biomarkers Specific levels, and (c) bladder cancer negative reference levels of one or more biomarkers. The results of the comparison demonstrate the efficacy of the composition for the treatment of bladder cancer.

Therefore, to characterize the efficacy of a composition for treating bladder cancer, the level of one or more biomarkers in a biological sample is compared to: (1) bladder cancer positive reference level, (2) bladder cancer negative a reference level, and (3) prior levels of the one or more biomarkers in the subject prior to treatment with the composition.

When the level of one or more biomarkers in a biological sample (from a subject with bladder cancer and currently or previously treated with the composition) is compared with the bladder cancer positive reference level and/or the bladder cancer negative reference level For comparison, the level in the sample that matches the bladder cancer negative reference level (e.g. the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum and/or maximum value of the reference level and/or or a level within the range of the reference level) indicates that the composition has efficacy in treating bladder cancer. The level of one or more biomarkers in the sample that matches a positive reference level for bladder cancer (e.g., the same as the reference level, substantially the same as the reference level, higher and/or lower than the minimum value of the reference level, and/or or a maximum value and/or a level within the range of the reference level) indicates that the composition has no efficacy in treating bladder cancer. The comparison may also indicate the degree of efficacy of treating bladder cancer based on the level of one or more biomarkers.

When the level of one or more biomarkers in a biological sample (from a subject with bladder cancer and currently or previously treated with the composition) is compared with a previously collected biological sample from the subject before treatment with the composition When the levels of the one or more biomarkers in the samples are compared, any change in the levels of the one or more biomarkers is indicative of the efficacy of the composition for treating bladder cancer. That is, if the comparison shows that the level of one or more biomarkers increases or decreases after treatment with the composition to become more similar to the bladder cancer negative reference level (or less similar to the bladder cancer positive reference level ), the result shows that the composition has the effect of treating bladder cancer. If the comparison shows that the level of one or more biomarkers is not increased or decreased to become more similar to the bladder cancer negative reference level (or less similar to the bladder cancer positive reference level) after treatment with the composition, then The results indicated that the composition had no efficacy in treating bladder cancer. The comparison may also indicate the degree of efficacy of treating bladder cancer in terms of the amount of change observed in the levels of one or more biomarkers following treatment. To aid in characterizing such comparisons, changes in the levels of one or more biomarkers, levels of one or more biomarkers prior to treatment, and/or subjects currently or previously treated with the composition The level of one or more biomarkers is compared with the bladder cancer positive reference level and/or the bladder cancer negative reference level.

Another method for assessing the efficacy of the composition in the treatment of bladder cancer comprises (1) analyzing a first biological sample of the subject to determine a selected from Tables 1, 5, 7, 9, 11 and/or 13 Levels of one or more biomarkers, the first sample is obtained from a subject at a first time point, (2) administering the composition to the subject, (3) analyzing a second biological sample from the subject To determine the level of one or more biomarkers, the second sample is obtained from the subject at a second time point after administration of the composition, and (4) one or more biological markers in the first sample The levels of the markers are compared to the levels of one or more biomarkers in a second sample to assess the efficacy of the composition for treating bladder cancer. As described above, if the comparison of the samples shows that the level of one or more biomarkers increases or decreases after administration of the composition to become more similar to the bladder cancer negative reference level, the results indicate that the composition has the effect of treating bladder cancer. effect. If the comparison shows that the level of one or more biomarkers is not increased or decreased to become more similar to the bladder cancer negative reference level (or less similar to the bladder cancer positive reference level) after treatment with the composition, then The results indicated that the composition had no efficacy in treating bladder cancer. The comparison may also indicate the degree of efficacy of treating bladder cancer in terms of the amount of change observed in the levels of one or more biomarkers following administration of the composition as described above.

The method of evaluating the relative efficacy of two or more compositions for treating bladder cancer comprises (1) analyzing a first biological Sample to determine the level of one or more biomarkers selected from Tables 1, 5, 7, 9, 11 and/or 13, (2) analyze samples from patients with bladder cancer and currently or previously treated with the second composition of a second biological sample from a second subject to determine the level of the one or more biomarkers, and (3) comparing the level of the one or more biomarkers in the first sample to the level of the second sample The levels of one or more biomarkers are compared to assess the relative efficacy of the first and second compositions for treating bladder cancer. The results indicate the relative efficacy of the two compositions, and the results (or the level of one or more biomarkers in the first sample and/or the level of one or more biomarkers in the second sample) can be compared ) are compared with bladder cancer positive reference level, bladder cancer negative reference level to assist in characterizing relative efficacy.

Various methods of assessing efficacy can be performed on one or more subjects or one or more groups of subjects (eg, a first group is treated with the first composition and a second group is treated with the second composition).

As with the other methods described herein, various techniques, including simple comparisons, one or more statistical analyzes and combinations thereof, can be employed in methods of evaluating the efficacy (or relative efficacy) of a composition for treating bladder cancer comparisons made. An example of a technique that may be employed is determining a subject's BCA score. Any suitable method can be used to analyze a biological sample to determine the level of one or more biomarkers in the sample. Additionally, the level of one or more biomarkers, including combinations of all biomarkers in Tables 1, 5, 7, 9, 11, and/or 13, or any fraction thereof, can be determined and used to evaluate combinations for the treatment of bladder cancer The efficacy (or relative efficacy) of substances.

Finally, methods of evaluating the efficacy (or relative efficacy) of one or more compositions for treating bladder cancer may also include analyzing a biological sample to determine the level of one or more non-biomarker compounds. The non-biomarker compound can then be compared to a reference level of the non-biomarker compound in a subject with (or without) bladder cancer.

F. Methods of Screening Compositions for Activity in Modulating Biomarkers Associated with Bladder Cancer

Identification of biomarkers for bladder cancer also allows screening of compositions useful in the treatment of bladder cancer for activity in modulating biomarkers associated with bladder cancer. Methods of screening compositions useful for treating bladder cancer include testing compositions for activity in modulating the levels of one or more biomarkers in Tables 1, 5, 7, 9, 11 and/or 13. Such screening assays can be performed in vitro and/or in vivo, and can be of any format known in the art for determining modulation of the biomarker in the presence of the test composition, such as cell culture assays, organ culture assays, and in vivo assays. Assays (eg, assays involving animal models).

In one embodiment, the method of screening a composition for activity in modulating one or more biomarkers of bladder cancer comprises (1) contacting one or more cells with the composition, (2) analyzing a or at least a portion of a plurality of cells or a biological sample related to cells to determine the level of one or more biomarkers of bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13; and (3 ) comparing the level of the one or more biomarkers to a predetermined standard level of the one or more biomarkers to determine whether the composition modulates the level of the one or more biomarkers. As noted above, the cells can be contacted with the composition in vitro and/or in vivo. The predetermined standard level of one or more biomarkers can be the level of one or more biomarkers in one or more cells in the absence of the composition. The predetermined standard level of one or more biomarkers can also be the level of one or more biomarkers in control cells that have not been contacted with the composition.

Additionally, the method can also include analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level of one or more non-biomarker compounds for bladder cancer. The level of the non-biomarker compound can then be compared to a predetermined standard level of one or more non-biomarker compounds.

Any suitable method can be used to analyze at least a portion of one or more cells or a biological sample associated with cells to determine the level of one or more biomarkers (or the level of a non-biomarker compound). Suitable methods include chromatography (eg, HPLC, gas chromatography, liquid chromatography), mass spectrometry (eg, MS, MS-MS), ELISA, antibody linking, other immunochemical techniques, and combinations thereof. Additionally, one or more biomarkers can be measured indirectly, for example, by using an assay that measures the level of a compound(s) that correlates with the level of the biomarker (or non-biomarker compound) that needs to be measured levels of biomarkers (or levels of non-biomarker compounds).

G. Methods to Identify Potential Drug Targets

The identification of biomarkers for bladder cancer also allows the identification of potential drug targets for bladder cancer. A method for identifying a potential drug target for bladder cancer comprising (1) identifying one or more biomarkers associated with one or more bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13 biochemical pathways, and (2) identifying proteins (eg, enzymes) that affect at least one of the one or more identified biochemical pathways that are potential drug targets for bladder cancer.

Another method of identifying a potential drug target for bladder cancer comprises (1) identifying one or more bladder cancer biomarkers selected from Tables 1, 5, 7, 9, 11 and/or 13 and one or one or more biochemical pathways associated with a plurality of non-biomarker compounds for bladder cancer, and (2) identifying a protein that affects at least one of the one or more identified biochemical pathways that is a potential marker for bladder cancer drug target.

One or more biochemical pathways (eg, biosynthetic and/or metabolic (catabolic) pathways) associated with one or more biomarkers (or non-biomarker compounds) are identified. After identifying biochemical pathways, one or more proteins that affect at least one pathway are identified. Preferably those proteins are identified that affect more than one pathway.

Accumulation of one metabolite (eg, a pathway intermediate) may indicate the presence of a "blocking" metabolite downstream, and the blockage may result in low/absent levels of a downstream metabolite (eg, a product of a biosynthetic pathway). In a similar manner, the absence of a metabolite may indicate a "block" in the pathway upstream of the metabolite, either due to enzyme inactivity or non-function, or due to the unavailability of a biochemical intermediate that is a necessary substrate for the production of the product. Alternatively, elevated levels of metabolites may indicate genetic mutations that produce abnormal proteins, which lead to overproduction and/or accumulation of metabolites, which in turn lead to alterations in other related biochemical pathways and to dysregulation of normal flux through that pathway; additionally , the accumulation of biochemical intermediate metabolites may be toxic, or may impair the production of essential intermediates of the relevant pathways. It is possible that the relationship between the pathways is currently unknown and this data may reveal such a relationship.

For example, data suggest that metabolites in biochemical pathways involved in nitrogen excretion, amino acid metabolism, energy metabolism, oxidative stress, purine metabolism, and bile acid metabolism are enriched in subjects with bladder cancer. Additionally, polyamine levels were higher in cancer subjects, indicating increased levels and/or activity of the enzyme ornithine decarboxylase. Polyamines are known to act as mitotic agents and are associated with free radical damage. These observations suggest that pathways leading to the production of polyamines (or any abnormal biomarkers) would provide a variety of potential targets for drug discovery.

In another example, data indicate that metabolites in biochemical pathways involved in lipid membrane metabolism, energy metabolism, phase I and II liver detoxification, and adenosine metabolism are enriched in bladder cancer subjects. Additionally, phosphorylcholine levels are higher in cancer subjects, indicating increased levels and/or activity of sphingomyelinase. These observations suggest that pathways leading to the production of phosphorylcholine (or any abnormal biomarker) would provide a variety of potential targets for drug discovery.

Proteins identified as potential drug targets can then be used to identify compositions, including compositions for gene therapy, that may be potential candidates for the treatment of bladder cancer.

H. Methods of treating bladder cancer

The identification of biomarkers for bladder cancer also allows for the treatment of bladder cancer. For example, to treat a subject with bladder cancer, an effective amount of one or more bladder cancer biomarkers that is reduced in bladder cancer compared to healthy subjects without bladder cancer can be administered to the subject . Administerable biomarkers may comprise one or more of the biomarkers listed in Tables 1, 5, 7, 9, 11 and/or 13 that are decreased in bladder cancer. In some embodiments, the administered biomarker is one or more of the biomarkers listed in Tables 1, 5, 7, 9, 11, and/or 13 that are decreased in bladder cancer with a p-value of less than 0.10. In other embodiments, the biomarkers administered are those listed in Tables 1, 5, 7, 9, 11 and/or 13 that are reduced by at least 5%, at least 10%, at least 15%, at least 20% in bladder cancer , at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e. absent) of one or more biomarkers.

In one example, sphingomyelinase present in urine cleaves sphingomyelin to form phosphorylcholine and ceramide. Sphingomyelinase activity can be increased in bladder cancer subjects to process large amounts of sphingomyelin. As increased activity of an enzyme such as sphingomyelinase is associated with bladder cancer, administration of an inhibitor of sphingomyelinase activity represents one possible approach to the treatment of bladder cancer.

III. Other methods

Other methods of using the biomarkers described herein are also contemplated. For example, small molecule profiles comprising one or more of the biomarkers disclosed herein can be used to perform the assays described in US Pat. No. 7,005,255, US Pat. No. 7,329,489, US Pat. The methods of Patent No. 7,635,556, US Patent Application No. 11/728,826, US Patent Application No. 12/463,690, and US Patent Application No. 12/182,828.

In any of the methods listed herein, the biomarkers used may be selected from the biomarkers in Tables 1, 5, 7, 9, 11 and/or 13 with a p-value less than 0.05. Biomarkers for use in any of the methods described herein may also be selected from Tables 1, 5, 7, 9, 11 and/or 13, decreased in bladder cancer (compared to controls) or decreased in urological cancers (compared to Control) at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60% %, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e. absent) of the biomarkers; and/or Tables 1, 5, Increase in bladder cancer (compared to control or remission) or increase in remission (compared to control or bladder cancer) in 7, 9, 11 and/or 13 by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80% , at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150% or higher biomarkers.

Example

The invention will be further explained by the following illustrative examples, which are intended to be non-limiting.

I. General approach

A. Identification of the Metabolic Profile of Bladder Cancer

Each sample was analyzed to determine the concentrations of several hundred metabolites. Metabolites were analyzed using analytical techniques such as GC-MS (gas chromatography-mass spectrometry) and LC-MS (liquid chromatography-mass spectrometry). After appropriate quality control (QC), multiple aliquots were analyzed simultaneously in parallel, recombining the information from each analysis. Each sample is characterized by thousands of properties, which ultimately equate to hundreds of chemical species. The technique used enables the identification of new and chemically unnamed compounds.

B. Statistical Analysis

The data are analyzed using a T-test to identify a definable population or subpopulation (e.g., compared to a control biological sample or compared to patients with bladder cancer in remission) that exists at a level of difference that can be used to distinguish a definable population (e.g., bladder cancer from controls). Molecules (known named metabolites or unnamed metabolites) in biomarkers of bladder cancer biological samples). Other molecules (either known named metabolites or unnamed metabolites) in definable populations or subpopulations were also identified.

The data were also analyzed comparatively using one-way analysis of variance (ANOVA) to identify a definable population or subpopulation (e.g., compared to a control biological sample or Molecules (either known named metabolites or unnamed metabolites) in biomarkers of bladder cancer biological samples compared to patients with bladder cancer in remission. ANOVA is a statistical model used to test that the means of multiple groups (≥ 2) are equal. Each group can be the level of a single variable (called one-way ANOVA) or a combination of 2, 3 or more variables (two-way ANOVA, three-way ANOVA, etc.). The total variable effects are approximated by main effects and interaction terms. More specific hypotheses can then be tested using the contrast that the linear combination of test group means equals 0. Unlike the two-sample t-test, ANOVA can handle repeated measures/correlated observations. Additional molecules (either known named metabolites or unnamed metabolites) in definable populations or subpopulations were also identified.

The data were also analyzed using random forest analysis. Random forests give an estimate of how well individuals in a new data set can be classified into existing groups. Random forest analysis builds a set of classification trees based on sequential sampling of experimental units and compounds. Each observation is then classified according to the majority vote from all classification trees. Statistically, a classification tree divides observations into groups based on combinations of variables (in this case the variables are metabolites or compounds). There are many variations on the algorithms used to build classification trees. The tree algorithm searches for metabolites (compounds) that provide the largest split between the two groups. This produces nodes. Then at each node, use the metabolite that provides the best split and so on. If the node cannot improve, it stops at that node and any observations at that node are classified as a majority group.

Random forests perform classification based on a large number (e.g. thousands) of trees. Each tree is built using a subset of compounds and a subset of observations. The observations used to build the tree are called in-bag samples, and the remaining samples are called out-of-bag samples. The classification tree is built from the in-bag samples, and the out-of-bag samples are predicted from the tree. To get the final classification of an observation, the "votes" for each group are counted according to how many times it is an out-of-bag sample. For example, suppose observation 1 is classified as "control" by 2,000 trees, but as "disease" by 3,000 trees. Using "majority wins" as the criterion, the sample was classified as "disease".

The results of random forests are summarized in a confusion matrix. Rows correspond to true grouping and columns correspond to random forest classification. Thus, diagonal elements indicate correct classification. For 2 groups, 50% error may occur randomly, for 3 groups, 66.67% error may occur randomly, etc. The "out-of-bag" (OOB) error rate gives an estimate of how well applying a random forest model can correctly predict a new observation (eg whether the sample is from a diseased subject or a control subject).

It is also interesting to see which variable is more "important" in the final classification. The "importance plot" shows the top compounds ranked according to their importance. The average reduction in the accuracy measure was used to determine significance. Calculate the Mean Decrease Accuracy as follows: For each tree in the random forest, calculate the classification error based on out-of-bag samples. Each variable (metabolite) was then permuted and the resulting error for each tree calculated. The average of the differences between the two errors is then calculated. This mean was then scaled by dividing the standard deviation of these differences. The more important the variable, the higher the average reduced accuracy.

Regression analysis was performed using ridge logistic regression model. The ridge regression form of logistic regression sets limits on the sum of squared coefficients, i.e. if b1, b2, b3, etc. b2^2 + b3^2 + ... + bp^2 < c). This bound forces many coefficients down to zero, so the method also does variable selection.

C. Biomarker Identification

Mass spectrometry-based chemical identification methods are performed on each peak identified in the analysis (eg, GC-MS, LC-MS, LC-MS-MS), including peaks identified as statistically significant.

Example 1: Biomarkers for Bladder Cancer

Biomarkers were discovered by (1) analyzing urine samples from different groups of human subjects to determine the levels of metabolites in the samples, and then (2) performing statistical analysis of the results to determine differences between the 2 groups metabolites in.

Two studies were conducted to identify biomarkers for bladder cancer. In Study 1, 10 control urine samples collected from subjects without bladder cancer and 10 urine samples from subjects with bladder cancer (urothelial transitional cell carcinoma) were analyzed . Age, race, and gender were all strictly controlled to minimize the effect of variables that confound demographic effects. All subjects were Caucasian males. The mean age of the bladder cancer cohort was 71.1, and the mean age of the control cohort was 67.7. For age, the p-value of the paired t-test analysis was 0.2, which indicated that age was not significantly different between the 2 groups.

After determining the levels of metabolites, the data were analyzed using a univariate T-test (ie Welch's T-test). As listed in Table 1 below, analysis of the named compounds resulted in the identification of biomarkers that were elevated in the urine of bladder cancer patients compared to control subjects and in the urine of bladder cancer patients compared to control subjects. Decreased biomarkers in fluid.

Biomarkers were identified that were differentially present in urine samples from bladder cancer patients and control patients without bladder cancer. Table 1, columns 1-3 list the identified biomarkers, for each listed biomarker, include the biochemical name of the biomarker, in cancer compared to non-cancer subjects (TCC/control) The fold change (FC) of the biomarker (which is the ratio of the average level of the biomarker in the cancer sample to the average level of the control) and the p-value determined in the statistical analysis of the data about the biomarker (Table 1, p. 1-3 columns). Column 10 of Table 1 lists the internal identifiers (CompID) of the biomarker compounds in the internal chemical library of authentic standards. Metabolites with (*) indicate statistical significance (p < 0.1 ) in both the TCC/control comparison (Study 1) and the larger study described below (Study 2). Bold values indicate fold changes with p-value ≤ 0.1. Table 1 includes other data, which are fully explained below.

Table 1. Bladder Cancer Biomarkers in Urine

Examples of biomarker metabolites showing abundance profiles supporting their use as diagnostic biomarkers for bladder cancer include combinations of oncometabolites (glycerol-2-phosphate, isocitrate, , glycerophosphocholine (GPC), isobutyrylcarnitine/glycine, xanthuric acid) and new metabolites for bladder cancer (α-hydroxybutyric acid, N-acetylglutamic acid). Figure 1 provides a graphical representation of the fold change profile of the osmolality normalized abundance ratio between TCC and case controls for selected exemplary biomarker metabolites. Similar graphs can be drawn for metabolites of any of the biomarkers listed in Table 1.

In Study 2, urine samples collected from 89 control subjects without bladder cancer (normal), 66 subjects with bladder cancer (BCA) were analyzed by (1) , 58 subjects with hematuria (Hem), 48 subjects with renal cell carcinoma (RCC) and 58 subjects with prostate cancer (PCA) to determine the concentration of metabolites in samples Levels, and then (2) perform statistical analysis on the results to identify metabolites that differ between groups and discover biomarkers.

After determining the levels of metabolites, data were analyzed using one-way ANOVA comparisons. Three comparisons were used to identify biomarkers for bladder cancer: bladder cancer versus normal, bladder cancer versus hematuria, and bladder cancer versus renal cell carcinoma and prostate cancer. As listed in Table 1, analysis of the named compounds resulted in the identification of a) bladder cancer and normal (columns 4-5), b) bladder cancer and hematuria (columns 6-7), and/or c) bladder cancer Biomarkers differentially present between and renal cell carcinoma + prostate cancer (columns 8-9).

For each biomarker, Table 1 includes the biochemical name of the biomarker; the fold change (FC) of the biomarker in bladder cancer compared to non-bladder cancer subjects (BCA/Normal, BCA/Hematuria, and BCA/RCC + PCA), which is the ratio of the mean level of the biomarker in the bladder cancer sample compared to the non-bladder cancer mean level; and the p-value determined in the statistical analysis of the data for the biomarker. Column 10 of Table 1 lists the internal identifier (CompID) of the biomarker compound in the authentic standard internal chemical library. Metabolites with (*) indicate statistical significance in the above two studies. Values in bold indicate fold change with p-value ≤ 0.1.

Example 2. Classification of Subjects Based on Urine Biomarkers in a Statistical Model

A. BCA and non-cancer

A number of analytical methods are available to evaluate the utility of identified biomarkers for diagnosing a patient condition (eg, whether a patient has bladder cancer). Two simple methods are used below: principal component analysis and hierarchical clustering using Pearson correlation.

In one method of analysis, principal component analysis was performed to model the classification of subjects as controls (non-cancer) or bladder cancer (TCC). The data used for the principal component analysis model were obtained from the osmolarity-normalized data of the urine samples from Study 1 of Example 1 (i.e., 10 control urine samples collected from subjects without bladder cancer and 10 urine samples from subjects with bladder cancer (urothelial transitional cell carcinoma).

Using the model derived by principal component analysis, it was found that 7 out of 10 samples from control subjects were correctly classified as controls, while 7 out of 10 samples from bladder cancer subjects were classified according to the measured biomarker levels. Correctly classified as bladder cancer. The model determines median values for some individuals. Individuals with intermediate values were not assigned to one of the two groups. The middle group consisted of 6 subjects, 3 were controls and 3 were bladder cancer patients. A graphical representation of the PCA results is provided in Figure 2.

In another statistical analysis, the osmolality-normalized biomarker values obtained from Study 1 of Example 1 (i.e., 10 control urine samples collected from subjects without bladder cancer and from Ten urine samples from subjects with bladder cancer (urothelial transitional cell carcinoma), BCA and non-cancer control subjects were classified using hierarchical clustering (Pearson correlation). This analysis resulted in subjects being divided into 3 different groups. One group consisted of 100% control individuals, one group consisted of 100% bladder cancer patients, and one group consisted of 33% controls and 67% bladder cancer patients. Figure 3 provides an illustration of hierarchical clustering results.

The results of PCA and hierarchical clustering models provide evidence for the existence of multiple metabolic types of bladder disease and/or bladder cancer that are distinguishable using urine biomarker metabolite levels. For example, cancer patients identified in the middle group may have a less aggressive form of bladder cancer, or may be an earlier stage of cancer. Distinguishing cancer types (such as less aggressive versus more aggressive) and cancer stage can be valuable information for doctors to determine the course of treatment.

In another analysis, the biomarkers identified in Example 1 were evaluated using random forest analysis to classify subjects as normal or with BCA. Urine samples from 66 BCA subjects and 89 normal subjects (subjects not diagnosed with BCA or other urological cancers) were used for this analysis.

The random forest results show that the samples are classified with 84% prediction accuracy. The confusion matrix provided in Table 2 shows the predicted number of samples for each category and the actual number for each group (BCA or normal). The "out-of-bag" (OOB) error rate gives an estimate of how accurately applying a random forest model predicts new observations (e.g. whether a sample is from a diseased subject or a control subject). The OOB error of this random forest is about 16%, and the model estimates that when used on a new set of subjects, it can accurately predict the characteristics of normal subjects 87% of the time and 80% of the time Bladder cancer subjects.

Table 2. Random Forest Results: Bladder Cancer vs. Normal

Based on an OOB error rate of 16%, the established random forest model predicted with approximately 84% accuracy whether a sample was from an individual with bladder cancer based on the levels of biomarkers measured in the subjects' samples. Exemplary biomarkers to differentiate groups are adenosine 5'-monophosphate (AMP), 3-hydroxyphenylacetic acid, 2-hydroxyhippuric acid (salicyluric acid), 3-hydroxyindolesulfate, phenylacetylglutamine Amide, p-cresol sulfate, 3-hydroxyhippuric acid, lactic acid, itaconic acid methylene succinic acid, cortisol, isobutyryl glycine, gluconic acid, xanthuric acid, guluronic acid 1,4-lactone, 3 -Hydroxybutyric acid (BHBA), cinnamoyl glycine, 2-oxindole-3-acetic acid, 2-hydroxybutyric acid (AHB), 1-2-propanediol, α-CEHC-glucuronide, palmitoyl sphingomyelin, Catechol Sulfate, Gamma-Glutamyl Phenylalanine, 2-Isopropyl Malate, Succinic Acid, 4-Hydroxyphenylacetic Acid, Pyridoxic Acid, Isovaleryl Glycine, Carnitine, and Tartaric Acid.

Random forest analysis confirmed that BCA subjects were distinguished from normal subjects with 80% sensitivity, 87% specificity, 82% PPV, and 86% NPV by using the biomarkers.

B. BCA and other urological cancers

The biomarkers in Table 1 were used to build a statistical model to classify subjects as having BCA or other urological cancer. Applying random forest analysis, the biomarkers were used in a mathematical model to classify subjects as having BCA or having PCA or RCC. Urine samples from 66 BCA subjects and 106 PCA or RCC subjects were used for this analysis.

The random forest results showed that the samples were classified with 83% prediction accuracy. The confusion matrix provided in Table 3 shows the predicted number of samples for each class and the actual number for each group (BCA or PCA+RCC). The "out-of-bag" (OOB) error rate gives an estimate of how accurately applying the random forest model can predict new observations (eg whether the sample is from a bladder cancer subject or a subject with PCA or RCC). The OOB error of Random Forest is about 17%, and the model estimates that when used on a new set of subjects, it can accurately predict the characteristics of BCA subjects 85% of the time and 82% of the time PCA+RCC subjects.

Table 3. Random Forest Results: Bladder Cancer vs. PCA+RCC

Based on an OOB error rate of 17%, the established random forest model predicted with approximately 83% accuracy whether a sample was from an individual with bladder cancer based on the levels of the biomarkers measured in the subjects' samples. Exemplary biomarkers to differentiate groups are imidazole-propionic acid, 3-hydroxyindole sulfate, phenylacetylglycine, lactate, choline, methyl-indole-3-acetic acid, beta-alanine, palmitoyl Sphingomyelin, 2-hydroxyisobutyric acid, succinic acid, 4-androstene-3β-17β-diol-disulfate-2, 4-hydroxyphenylacetic acid, glycerol, uracil, gulonic acid 1,4- Lactone, phenol sulfate, dimethylarginine (ADMA + SDMA), cyclo-gly-pro, sucrose, adenosine, serine, azelaic acid (nonanedioic acid), threonine, pregnanediol-3 - Glucuronide, ethanolamine, gluconic acid, N6-methyladenosine, N-methylproline, glycine, glucose 6-phosphate (G6P).

Random forest results showed that BCA subjects were distinguished from PCA+RCC subjects with 85% sensitivity, 82% specificity, 75% PPV, and 90% NPV by using the biomarkers.

C. BCA and hematuria

The biomarkers in Table 1 were used to build a statistical model to classify subjects as having BCA or hematuria. Using random forest analysis, biomarkers were used in a mathematical model to classify subjects as having BCA or hematuria. Urine samples from 66 BCA and 58 hematuria patients were used for analysis.

The random forest results showed that the samples were classified with 74% prediction accuracy. The confusion matrix provided in Table 4 shows the predicted number of samples for each category and the actual number for each group (BCA or hematuria). The "out of bag" (OOB) error rate gives an estimate of how accurately applying the random forest model can predict new observations (eg whether the sample is from a bladder cancer subject or a subject with hematuria). The OOB error of Random Forest is about 26%, and the model estimates that when used on a new set of subjects, it can accurately predict the characteristics of BCA subjects 70% of the time and 79% of the time subjects with hematuria.

Table 4. Random Forest Results: Bladder Cancer and Hematuria

Based on an OOB error rate of 26%, the established random forest model predicted with approximately 74% accuracy whether the sample was from an individual with bladder cancer based on the analysis of the measured biomarker levels in the subjects' samples. Exemplary biomarkers used to differentiate the groups are isovalerylglycine, 2-hydroxybutyric acid (AHB), 4-hydroxyhippuric acid, gluconic acid, gulonic acid 1,4-lactone, 3-hydroxyhippuric acid Uric Acid, Tartaric Acid, 2-Hydroxyindole-3-Acetic Acid, Isobutyryl Glycine, Catechol Sulfate, Phenylacetyl Glutamine, Succinic Acid, 3-Hydroxybutyric Acid (BHBA), Cinnamoyl Glycine, Isobutyryl Glycine Alkali, 3-hydroxyphenylacetic acid, 3-hydroxyindole sulfate, sorbose, 2-5-furandicarboxylic acid, methyl-4-hydroxybenzoic acid, 2-isopropylmalic acid, adenosine 5'-monophosphate (AMP), 2-methylbutyrylglycine, palmitoylsphingomyelin, phenylpropionylglycine, beta-hydroxypyruvate, tyramine, 3-methylcrotonylglycine, carnosine, fructose.

Random forest results showed that BCA subjects were distinguished from hematuria subjects with 70% sensitivity, 79% specificity, 79% PPV, and 70% NPV by using the biomarkers.

Example 3. Biomarkers for bladder cancer staging

Bladder cancer staging provides an indication of how far the bladder tumor has spread. Tumor staging is used to select treatment options and estimate a patient's prognosis. Bladder tumor stages range from T0 (no evidence of a primary tumor, the earliest stage) to T4 (the tumor has spread beyond the fatty tissue surrounding the bladder into nearby organs, the latest stage). Early stages of bladder cancer can also be characterized as carcinoma in situ (CIS), which means cells that proliferate abnormally but are still contained within the bladder.

To identify biomarkers of disease stage and/or progression, 21 low-stage BCA (CIS, T0, T1) subjects, 42 high-stage BCA (T2-T4) subjects, and 89 normal subjects were tested. Urine samples from the patients were subjected to metabolome analysis. After determining the levels of metabolites, the data were analyzed using one-way ANOVA comparatively to identify biomarkers that differ in: 1) low-stage bladder cancer vs. normal, 2) high-stage bladder cancer vs. normal , and/or 3) low-stage bladder cancer compared to high-stage bladder cancer. The identified biomarkers are listed in Table 5.

For each biomarker, Table 5 includes the biochemical name of the biomarker, the fold change of the biomarker for: 1) low-stage BCA vs. normal, 2) high-stage BCA vs. normal, 3) low-stage BCA vs. normal, Stage BCA versus high stage BCA, and 4) bladder cancer versus subjects with a history of bladder cancer (Example 4) and p-values determined in the statistical analysis of the data on biomarkers. Column 10 of Table 5 contains the internal identifier (CompID) of the biomarker compound in the internal chemical library of the authentic standard. Values in bold indicate fold change with p-value ≤ 0.1.

Table 5. Biomarkers for Staging and Monitoring of Bladder Cancer

Example 4. Biomarkers for Monitoring Bladder Cancer

To identify biomarkers for monitoring bladder cancer, urine samples were collected from 119 subjects with a history of bladder cancer but no indication of bladder cancer at the time of urine collection (HX) and 66 subjects with bladder cancer. Metabolome analysis was performed. After determining the levels of metabolites, the data were analyzed using one-way ANOVA comparatively to identify biomarkers that differed between patients with a history of bladder cancer and normal subjects. Biomarkers are listed in columns 1, 8, and 9 of Table 5.

The biomarkers in Table 5 were used to build statistical models to classify subjects into BCA or HX groups. Random forest analysis was used to classify subjects as having bladder cancer or having a history of bladder cancer.

The random forest results showed that the samples were classified with 83% prediction accuracy. The confusion matrix provided in Table 6 shows the predicted number of samples for each class and the actual number for each group (BCA or HX). The "out-of-bag" (OOB) error rate gives an estimate of how accurately applying the random forest model can predict new observations (e.g. whether the sample is from a subject with bladder cancer or a subject with a history of bladder cancer). Random forests had an OOB error of about 17%, and the model estimated that when applied to a new set of subjects, it accurately predicted the characteristics of bladder cancer subjects 76% of the time and 87% of the time Subjects with a history of bladder cancer.

Table 6. Random Forest Results: Bladder Cancer vs Bladder Cancer History

Based on an OOB error rate of 17%, the established random forest model predicted with approximately 83% accuracy whether the sample was from an individual with bladder cancer based on the analysis of the measured biomarker levels in the subjects' samples. Exemplary biomarkers used to differentiate groups are 3-hydroxyphenylacetic acid, 3-hydroxyhippuric acid, 3-hydroxybutyric acid (BHBA), isovalerylglycine, phenylacetylglutamine, pyridoxic acid, 2- 5-furandicarboxylic acid, allantoin, pimelic acid (heptanedioic acid), lactic acid, adenosine 5'-monophosphate (AMP), catechol sulfate, 2-hydroxybutyric acid (AHB), isobutyryl Glycine, 2-hydroxyhippuric acid (salicyluric acid), gluconic acid, imidazole-propionic acid, succinic acid, α-CEHC-glucoronide, 3-hydroxyindolesulfate, 4-hydroxyphenylacetic acid, Acetylcarnitine, xanthine, p-cresol sulfate, tartaric acid, 4-hydroxyhippuric acid, 2-isopropylmalic acid, palmitoyl sphingomyelin, adipic acid and N(2)-furoyl-glycine.

Random forest results showed that BCA subjects were distinguished from HX subjects with 76% sensitivity, 87% specificity, 77% PPV, and 87% NPV by using the biomarkers.

Example 5. Tissue biomarkers for bladder cancer.

Discovery of biomarkers by (1) analyzing tissue samples from different groups of human subjects to determine the levels of metabolites in the samples, and then (2) performing statistical analysis of the results to identify metabolites that are differentially present in each group .

The samples used for analysis were: 31 control (benign) samples and 98 bladder cancer (tumors).

After determining the levels of metabolites, the data were analyzed using Welch's two-sample t-test. To identify biomarkers for bladder cancer, benign samples were compared with bladder cancer samples. As listed in Table 7 below, analysis of the named compounds resulted in the identification of biomarkers that were differentially present between bladder cancer and control tissues.

For each biomarker, Table 7 includes the biochemical name of the biomarker, the fold change of the biomarker in bladder cancer compared to control samples (BCA/control) (which is the average level of the biomarker in bladder cancer samples ratio compared to the non-bladder cancer mean) and p-values determined in the statistical analysis of the data on the biomarkers. Columns 4-6 of Table 7 list the following aspects: internal identifiers (CompID) of biomarker compounds in the internal chemical library of reliable standards; The identifier of the biomarker compound, if available; and the identifier of the biomarker compound in the Human Metabolome Database (HMDB), if available.

Table 7. Tissue Biomarkers for Bladder Cancer

Statistical models are built using biomarkers to classify subjects. Biomarkers were evaluated using random forest analysis to classify samples as bladder cancer or controls. The random forest results showed that the samples were classified with 84% prediction accuracy. The confusion matrix provided in Table 8 shows the predicted number of samples for each class and the actual number for each group (BCA or control). The "out-of-bag" (OOB) error rate gives an estimate of how accurately applying the random forest model can predict new observations (e.g. whether a sample is a BCA or a control sample). The OOB error was about 15%, and the model estimated that when applied to a new set of subjects, it could predict the characteristics of bladder cancer subjects 87% of the time and accurately predict the characteristics of control subjects 77% of the time. For those, see Table 8.

Table 8. Results of Random Forest: Bladder Cancer vs Control

Based on a 16% OOB error rate, by measuring the levels of biomarkers in the subjects' samples, the established random forest model predicted with about 85% accuracy whether the samples were from individuals with cancer. Exemplary biomarkers used to differentiate groups are gluconic acid, 6-phosphogluconic acid, stearoyl sphingomyelin, inositol, glucose, 3-(4-hydroxyphenyl)lactic acid (HPLA), 1- Oleoylglycerol (1-monolinolein), pro-hydroxy-pro, gamma-glutamyl glutamate, creatine, 5,6-dihydrouracil, docosadienoic acid (22:2n6 ), phenyl lactic acid (PLA), propionyl carnitine, isoleucyl proline, N2-methylguanosine, eicosapentaenoic acid (EPA 20:5n3), 5-methylthioadenosine (MTA ), α-glutamyl lysine, 3-phosphoglycerate, 6-ketoprostaglandin F1α, docosatrienoic acid (22:3n3), 2-palmitoleyl glycerophosphocholine, 1-hard Fatty acylglyceroinositide, 1-palmitoylglyceroinositol, scyllo-inositol, dihomo-linoleic acid (20:2n6), 3-phosphoserine, docosapentaenoic acid (n6 DPA 22: 5n6) and 1-palmitoylglycerol (1-glycerol monopalmitate).

Random forest results showed that bladder cancer samples were distinguished from control samples with 87% sensitivity, 77% specificity, 92% PPV, and 65% NPV by using the biomarkers.

Example 6. Tissue biomarkers for bladder cancer staging.

Bladder cancer staging provides an indication of how far bladder cancer has spread. Tumor staging is used to select treatment options and estimate a patient's prognosis. Bladder tumor stages range from T0 (no evidence of a primary tumor, the earliest stage) to T4 (the tumor has spread beyond the fatty tissue surrounding the bladder into nearby organs, the latest stage).

To identify biomarkers of disease stage and/or progression, tissue samples from 17 subjects with low-stage BCA (T0a, T1), 31 subjects with high-stage BCA (T2-T4), and 44 subjects with Metabolome analysis was performed in benign (control) tissue samples. After determining the levels of metabolites, the data were analyzed using Welch's two-sample t-test to identify biomarkers that differ in: 1) low-stage bladder cancer versus high-stage bladder cancer, 2) low-stage bladder cancer versus high-stage bladder cancer compared to controls, and 3) high-stage bladder cancer compared to controls. Biomarkers are listed in Table 9.

For each biomarker, Table 9 includes the biochemical name of the biomarker; the fold change (FC) of the biomarker in the following: 1) High-stage bladder cancer compared to low-stage bladder cancer (T2-T4/Toa-T1 ), 2) low-stage bladder cancer compared to benign (T0a-T1/benign), 3) high-stage bladder cancer compared to benign (T2-T4/benign); and in statistical analyzes of data on biomarkers Determined p-value. Columns 8-10 of Table 9 list: the internal identifier (CompID) of the biomarker compound in the internal chemical library of reliable standards; the identifier of the biomarker compound from the Kyoto Encyclopedia of Genes and Genomes (KEGG), as available if available; and the identifier of the biomarker compound in the Human Metabolome Database (HMDB), if available. Values in bold indicate fold changes with p-values ≤ 0.1.

Table 9. Tissue Biomarkers for Bladder Cancer Staging

Statistical models are built using biomarkers to classify subjects. The biomarkers in Table 9 were evaluated using random forest analysis to classify samples as low-stage bladder cancer or high-stage bladder cancer. The random forest results showed that the samples were classified with 83% prediction accuracy. The confusion matrix provided in Table 10 shows the predicted number of subjects for each category and the actual number for each group (BCA high or BCA low). The "out-of-bag" (OOB) error rate gives an estimate of how accurately applying a random forest model can predict new observations (e.g. whether a sample is from a subject with low-stage bladder cancer or from a subject with high-stage bladder cancer). subjects). The OOB error was approximately 17%, and the model estimated that when applied to a new group of subjects, it could predict the characteristics of high-stage bladder cancer subjects 84% of the time and accurately predict low-stage bladder cancer 82% of the time. Stage bladder cancer subjects, see Table 10.

Table 10. Results of random forest: low-stage BCA vs high-stage BCA

Based on an OOB error rate of 17%, the established random forest model predicted with approximately 83% accuracy whether the sample was from an individual with RCC by measuring the biomarker levels in the subjects' samples. Exemplary biomarkers used to differentiate groups are palmitoyl ethanolamide, palmitoyl sphingomyelin, thromboxane B2, bilirubin (Z,Z), adrenaline (22:4n6), C-glycosyl tryptophan, Methyl-α-Glucopyranoside, Methyl Phosphate, 3-Hydroxydecanoic Acid, 3-Hydroxycaprylic Acid, 4-Hydroxyphenylpyruvate, N-Acetyl Threonine, 1-Arachidonoylglycerol Phosphate Alcohol (20:4), 5 6-dihydrothymine, 2-hydroxypalmitic acid, coenzyme A, N-acetylserine, nicotinamide adenine dinucleotide (NAD+), docosatrienoic acid ( 22:3n3), reduced glutathione (GSH), prostaglandin A2, glutamine, glutamic acid γ-methyl ester, docosapentaenoic acid (n6 DPA 22:5n6), glycosaminoglycan Deoxycholic acid, caproylcarnitine, arachidonic acid (20:4n6), pro-hydroxy-pro, docosahexaenoic acid (DHA 22:6n3) and laurylcarnitine.

Random forest results showed that RCC subjects were distinguished from normal subjects with 84% sensitivity, 82% specificity, 90% PPV, and 74% NPV by using the biomarkers.

Example 7. Biomarker panels and mathematical models for the identification of bladder cancer.

In another example, a panel of 5 exemplary biomarkers selected from the biomarkers identified in Table 1 and/or Table 5 is selected to identify bladder cancer. The identified biomarkers were present at levels that differed between BCA and each individual comparison group (ie, BCA versus normal, HX, hematuria, RCC, and PCA). For example, lactate, palmitoyl sphingomyelin, phosphorylcholine, succinate, and adenosine are important biomarkers for differentiating bladder cancer subjects from normal, HX, hematuria, RCC, and PCA subjects. All biomarker compounds used for these analyzes were statistically significant (p<0.05). For each listed biomarker, Table 11 includes the biochemical name of the biomarker; the fold change of the biomarker for: 1) bladder cancer subjects compared to normal subjects (BCA/NORM), 2 ) subjects with bladder cancer compared to subjects with a history of bladder cancer (BCA/HX), 3) subjects with bladder cancer compared to subjects with hematuria (BCA/HEM), 4) subjects with bladder cancer subjects compared to kidney cancer subjects (BCA/RCC), 5) bladder cancer subjects compared to prostate cancer subjects (BCA/PCA); and in relation to BCA compared to normal biomarkers The p-values were determined in the statistical analysis of the data.

Table 11. Identification of biomarkers for bladder cancer

Next, the biomarkers in Table 11 were used in the mathematical model according to ridge logistic regression analysis. The Ridge Regression method builds a statistical model that can be used to evaluate biomarker compounds associated with disease and the evaluation can be used to classify individuals as, for example, with BCA or not, with BCA or normal (no cancer), with Biomarker compounds for BCA or having hematuria, having BCA or having a history of BCA. The predictive performance (eg, the ability of the mathematical model to correctly classify a sample as cancer or non-cancer) of the five biomarkers identified in Table 11 was determined using ridge logistic regression analysis. Table 12 shows the AUC of the five bladder cancer biomarkers compared to the permuted AUC (that is, the AUC of the null hypothesis). The average of the permuted AUCs represents the expected value of AUCs that could only be obtained by chance. For all comparisons, the 5 biomarkers listed in Table 11 predicted bladder cancer with a higher accuracy than that obtained with the 5 metabolites that had no real relevance for the comparison (ie 5 metabolites selected at random). A graphical representation of the resulting receiver operating characteristic (ROC) curve is shown in FIG. 4 .

Table 12. Predictive performance of biomarkers for bladder cancer

Compare Average AUC ridge per permutation 5 Biomarker Ridges BCA and HX 0.711 0.821 BCA and NORM 0.724 0.823 BCA and all other groups 0.674 0.799 BCA and HEM 0.75 0.791

In another example, a panel of 7 exemplary biomarkers selected from the biomarkers identified in Table 1 and/or Table 5 is selected to identify bladder cancer. The identified biomarkers were present at levels that differed between BCA and each individual comparison group (ie, BCA versus normal, HX, hematuria) as shown in Table 13. For example, 1,2 propanediol, adipic acid, anserine, 3-hydroxybutyric acid (BHBA), pyridoxic acid, acetylcarnitine and 2-Hydroxybutyrate (AHB) was a significant (p<0.05) biomarker. All biomarker compounds used for these analyzes were statistically significant (p<0.05). For each listed biomarker, Table 13 includes the biochemical name of the biomarker; the fold change of the biomarker for: 1) bladder cancer subjects compared to normal subjects (BCA/NORM), 2 ) subjects with bladder cancer compared to subjects with a history of bladder cancer (BCA/HX), and 3) subjects with bladder cancer compared to subjects with hematuria (BCA/HEM).

Table 13. Biomarkers Distinguishing BCA from Non-Cancer (Hematuria, HX, Normal)

Biomarkers BCA/Normal BCA/HX BCA/hematuria 1,2-propanediol 5.37 3.11 5.95 Adipic acid 4.53 5.02 4 Anserine 0.23 0.14 0.23 3-Hydroxybutyric acid (BHBA) 18.95 24.27 19.58 pyridoxic acid 0.33 0.3 0.5 Acetylcarnitine 2.39 2.63 2.45 2-Hydroxybutyric acid (AHB) 2.96 3.29 2.04

Next, the biomarkers in Table 13 were used in the mathematical model based on ridge logistic regression analysis. The Ridge Regression method builds a statistical model that can be used to evaluate biomarker compounds associated with disease and the evaluation can be used to classify individuals as, for example, having BCA or being normal (no cancer), having BCA, or having hematuria. , biomarker compounds with or with a history of BCA. The predictive performance (eg, the ability of the mathematical model to correctly classify a sample as cancer or non-cancer) of the seven biomarkers identified in Table 13 was determined using ridge logistic regression analysis. The AUC of the seven bladder cancer biomarkers was 0.849 [95% CI, 0.794-0.905]. An illustration of the ROC curve is shown in Figure 5. For all comparisons, the 7 biomarkers listed in Table 13 predicted bladder cancer with a higher accuracy than that obtained with the 5 metabolites that had no real relevance for the comparisons.

In another example, 5 biomarkers listed in Table 11 and 7 biomarkers listed in Table 13 are used in combination with one or more exemplary biomarkers identified in Table 1 and/or Table 5 A subset of , an exemplary panel of biomarkers was selected to identify bladder cancer subjects and non-bladder cancer subjects. In this example, kynurenine was selected as an exemplary biomarker in Table 1 and/or Table 5 (kynurenine is in both Tables 1 and 5). The resulting marker panel thus included 13 listed metabolites: lactate, palmitoyl sphingomyelin, phosphorylcholine, succinate, adenosine, 1,2 propanediol, adipate, anserine, 3-hydroxybutyrate, Pyridoxic Acid, Acetyl Carnitine, AHB and Kynurenine.

Next, 13 biomarkers were used in the mathematical model based on ridge logistic regression analysis. A ridge regression approach was used to develop a statistical model useful for evaluating biomarker compounds associated with disease and for evaluating biomarker compounds useful for classifying individuals as, for example, having BCA or not having cancer (i.e., normal, hematuria, or history of BCA) . Using ridge logistic regression analysis, the predictive performance of various combinations of 13 biomarkers selected from two or more of the following biomarkers was determined: lactate, palmitoyl sphingomyelin, phosphorylcholine, succinate adenosine, 1,2-propanediol, adipic acid, anserine, 3-hydroxybutyric acid, pyridoxic acid, acetylcarnitine, AHB, or kynurenine. The AUC for each panel of biomarkers for bladder cancer ranged from 0.85 (for the two biomarker model) to 0.9 (for the model consisting of 10-12 biomarkers). See Figure 6 for a graph of the AUC obtained for each group with the ridge model.

In another example, a panel of 11 exemplary biomarkers is selected to identify bladder cancer or hematuria in a subject. In this example, the biomarker panel included tyramine, palmitoyl sphingomyelin, phosphorylcholine, adenosine, 1,2 propanediol, adipic acid, BHBA, acetylcarnitine, AHB, xanthuric acid, and succinic acid. Using ridge logistic regression analysis, the predictive performance (that is, the ability of the mathematical model to correctly classify a sample as cancer or hematuria) of the 11 biomarkers was determined. The AUC of 11 biomarkers was 0.886 [95% CI, 0.831-0.941]. See Figure 7 for an illustration of the ROC curve. For all comparisons, 11 biomarkers predicted bladder cancer with higher accuracy than obtained with metabolites that had no real relevance for the comparisons.

Next, 11 biomarkers were used in the mathematical model based on ridge logistic regression analysis. The ridge regression method builds a statistical model that can be used to evaluate biomarker compounds that are associated with disease and that can be used to classify individuals as having, for example, BCA or hematuria. Using ridge logistic regression analysis, the predictive performance of various combinations of 11 biomarkers selected from two or more of the following biomarkers (that is, the mathematical model correctly classifying the sample as ability for cancer or hematuria): tyramine, palmitoyl sphingomyelin, phosphorylcholine, adenosine, 1,2 propanediol, adipic acid, BHBA, acetylcarnitine, AHB, xanthuric acid, and succinic acid. The AUC for the biomarker groups for bladder cancer ranged from 0.82 (for the two biomarker model) to 0.886 (for the model consisting of 8-12 biomarkers). See Figure 8 for a graph of the AUC obtained for each group using the ridge model.

Example 8. Algorithm to Monitor Bladder Cancer Progression/Regression

Using biomarkers of bladder cancer, algorithms can be developed to monitor bladder cancer progression/regression in a subject. Based on a panel of metabolite biomarkers from Tables 1, 5, 7, 9, 11 and/or 13, the algorithm, when applied to a new group of patients, can evaluate and monitor the progression/regression of bladder cancer in patients. Using the results of this biomarker algorithm, medical oncologists can evaluate the risk-benefit of surgery (eg, transurethral resection, radical cystectomy, or segmental cystectomy), medical therapy, or watchful waiting.

A biomarker algorithm can be used to monitor the levels of a panel of bladder cancer biomarkers identified in Tables 1, 5, 7, 9, 11 and/or 13.

Example 9. Identification of drug targets and drug screening using said targets.

To identify drug targets for bladder cancer, 10 control urine samples collected from subjects without bladder cancer and 10 urine samples from subjects with bladder cancer (urothelial transitional cell carcinoma) were analyzed samples to determine the levels of metabolites in the samples, the results were then statistically analyzed using a univariate T-test (i.e. Welch test) to determine the metabolites that differed between the 2 groups, and then the differences were analyzed in a biological context Metabolic pathways of the metabolites present to identify relevant metabolites, enzymes and/or proteins.

Metabolites, enzymes and/or proteins associated with differentially occurring metabolites represent bladder cancer drug targets. Metabolite levels that are abnormal (higher or lower) in bladder cancer subjects relative to control (non-BCA) subjects can be adjusted to be in the normal range, which can be therapeutic. Such metabolites or enzymes involved in relevant metabolic pathways and proteins involved in intracellular and intercellular transport may provide targets for therapeutic agents.

For example, bladder cancer is associated with altered levels of biochemical intermediates in the tricarboxylic acid cycle (TCA), as well as biochemicals associated with all major ATP-producing pathways. In this example, changes in TCA cycle intermediates were found in subjects with bladder cancer, with significant effects on isocitrate and its intermediate downstream metabolites. Isocitrate levels were found to be statistically significantly higher in the urine of bladder cancer subjects. Thus, an agent that modulates isocitrate levels in urine may be a therapeutic agent. For example, the agent can modulate urinary isocitrate levels by reducing isocitrate biosynthesis. Bladder cancer also has a marked effect on TCA cycle intermediates between citrate and succinyl-CoA, especially isocitrate, α-ketoglutarate, and the two TCA α-ketoglutarate-derived metabolites 2-hydroxyglutarate diacid and glutamic acid. A graphical representation of these results is shown in Figure 9, which illustrates the TCA cycle. See box plots for levels of biochemicals measured in urine collected from control individuals and bladder cancer patients.

Urinary metabolite profiles of bladder cancer cases indicated that all major ATP-producing pathways were altered in bladder cancer, except for the TCA cycle. An elevated lactate/pyruvate ratio indicates Warburg-like utilization of glucose in bladder cancer patients. Increased ketone body production suggests increased fatty acid beta-oxidation in these patients. Finally, the decreased abundance of branched-chain acylcarnitines and acylglycines suggested that this pathway is differentially engaged in bladder cancer patients. Metabolites reporting activity in glycolysis, branched-chain amino acid catabolism, and fatty acid oxidation were all altered in bladder cancer cases compared to controls. Branched-chain acylcarnitines are shown as an alternative to branched-chain acyl-CoA compounds. Figure 10 provides a box plot illustrating these changes.

Identification of biomarkers for bladder cancer can be used to screen for therapeutic compounds. For example isocitrate, alpha-ketoglutarate or any of the biomarkers identified in Tables 1, 5, 7, 9, 11 and 13 that are abnormal in subjects with bladder cancer can be used for various drug screens technology.

One exemplary method of drug screening utilizes eukaryotic or prokaryotic host cells such as bladder cancer cells. In this prophetic example, cells were seeded into 96-well plates. Test wells were incubated in the presence of test compounds from the NIH Clinical Collection Library (available from BioFocus DPI) at a final concentration of 50 μΜ. Negative control wells received no addition or incubation with vehicle compound (eg, DMSO) at a concentration equivalent to that present in some test compound solutions. After 24 hours of incubation, the test compound solution was removed, metabolites were extracted from the cells, and isocitrate levels were measured as described in the General Methods section. Agents that lower isocitrate levels in cells are considered therapeutic agents.

Although the invention has been described in detail and with reference to specific embodiments thereof, it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention.

Claims (36)

1. A method of determining or assisting in determining whether a subject has bladder cancer, the method comprising: Analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein the one or more biomarkers are selected from Tables 1, 5, 7, 9, 11, and/or or 13, and The level of the one or more biomarkers in the sample is compared to bladder cancer positive and/or bladder cancer negative reference levels of the one or more biomarkers to determine whether the subject has bladder cancer. 2. The method of claim 1, wherein said sample is analyzed using one or more techniques selected from the group consisting of mass spectrometry, ELISA, and antibody linkage. 3. The method of claim 2, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 5, 7, 9, 11 and/or 13, analyzing the subjects and a biological sample from a subject. 4. The method of claim 1, wherein the one or more biomarkers are selected from the group consisting of anserine, pyridoxic acid, adipic acid, xanthuric acid, 1,2-propanediol, phosphorylcholine, acetylcarnitine, 3 -Hydroxybutyric acid (BHBA), palmitoyl sphingomyelin, tyramine, lactic acid, succinic acid, adenosine, 2-hydroxybutyric acid (AHB), kynurenine, adenosine 5'-monophosphate (AMP), 3 -Hydroxyphenylacetic acid, 2-hydroxyhippuric acid (salicyluric acid), 3-hydroxyindole sulfate, phenylacetylglutamine, p-cresol sulfate, 3-hydroxyhippuric acid, itaconic acid methylene succinic acid, Cortisol, isobutyrylglycine, gluconic acid, xanthuric acid, gulonic acid 1,4-lactone, cinnamoylglycine, 2-oxindole-3-acetic acid, α-CEHC-glucuronide, catechol Sulfuric acid, γ-glutamyl phenylalanine, 2-isopropyl malic acid, 4-hydroxyphenylacetic acid, isovaleryl glycine, carnitine, tartaric acid, 6-phosphogluconic acid, stearoyl sphingomyelin, muscle alcohol, glucose, 3-(4-hydroxyphenyl)lactic acid, 1-linoleoylglycerol (1-monolinolein), pro-hydroxy-pro, gamma-glutamyl glutamate, creatine, 5, 6-dihydrouracil, docosadienoic acid (22:2n6), phenyllactic acid (PLA), propionylcarnitine, isoleucylproline, N2-methylguanosine, eicosan Pentaenoic acid (EPA 20:5n3), 5-methylthioadenosine (MTA), α-glutamyl lysine, 3-phosphoglycerate, 6-keto prostaglandin F1α, docosatrienoic acid ( 22:3n3), 2-palmitoleyl glycerophosphocholine, 1-stearyl glycerophosphoinositol, 1-palmitoyl glycerophosphoinositol, scyllo-inositol, dihomo-linoleic acid (20:2n6) , 3-phosphoserine, docosapentaenoic acid (n6 DPA 22:5n6), 1-palmitoylglycerol and (1-glyceryl monopalmitate). 5. The method of claim 1, wherein the subject has hematuria and the one or more biomarkers are selected from Tables 1, 7, 11 and/or 13. 6. The method of claim 5, wherein the one or more biomarkers are selected from the group consisting of anserine, pyridoxic acid, adipic acid, xanthuric acid, 1,2-propanediol, phosphorylcholine, acetylcarnitine, 3 -Hydroxybutyric acid (BHBA), palmitoyl sphingomyelin, tyramine, lactic acid, isovalerylglycine, 2-hydroxybutyric acid (AHB), 4-hydroxyhippuric acid, gluconic acid, gulonic acid 1,4-endo Esters, 3-hydroxyhippuric acid, tartaric acid, 2-oxindole-3-acetic acid, isobutyryl glycine, catechol sulfate, phenylacetyl glutamine, succinic acid, cinnamoyl glycine, isobutyryl carnitine, 3 -Hydroxyphenylacetic acid, 3-hydroxyindole sulfate, sorbose, 2,5-furandicarboxylic acid, methyl-4-hydroxybenzoic acid, 2-isopropylmalic acid, adenosine 5'-monophosphate (AMP) , 2-methylbutyrylglycine, phenylpropionylglycine, beta-hydroxypyruvate, 3-methylcrotonylglycine, carnosine, fructose, adenosine and kynurenine. 7. The method of claim 1, wherein the subject has a history of bladder cancer and the one or more biomarkers are selected from Tables 1, 7, 11 and/or 13. 8. The method of claim 7, wherein the one or more biomarkers are selected from the group consisting of anserine, pyridoxic acid, adipic acid, xanthuric acid, 1,2-propanediol, phosphorylcholine, acetylcarnitine, 3 -Hydroxybutyric acid (BHBA), palmitoyl sphingomyelin, tyramine, lactic acid, 3-hydroxyphenylacetic acid, 3-hydroxyhippuric acid, isovalerylglycine, phenylacetylglutamine, 2,5-furandicarboxylic acid, urine Bursin, pimelic acid (heptanedioic acid), adenosine 5'-monophosphate (AMP), catechol sulfate, 2-hydroxybutyric acid (AHB), isobutyryl glycine, 2-hydroxyhippuric acid (water yanyluric acid), gluconic acid, imidazole-propionic acid, succinic acid, α-CEHC-glucuronic acid, 3-hydroxyindole sulfate, 4-hydroxyphenylacetic acid, xanthine, p-cresol sulfate, tartaric acid, 4-hydroxy Hippuric acid, 2-isopropylmalic acid, N(2)-furoyl-glycine, adenosine, and kynurenine. 9. The method of claim 1, wherein the subject has urological cancer and the biomarkers are selected from Tables 1, 7, 11 and/or 13. 10. The method of claim 9, wherein the one or more biomarkers are selected from the group consisting of anserine, pyridoxic acid, adipic acid, xanthuric acid, 1,2-propanediol, phosphorylcholine, acetylcarnitine, 3 -Hydroxybutyric acid (BHBA), palmitoyl sphingomyelin, tyramine, lactic acid, imidazole-propionic acid, 3-hydroxyindolesulfate, phenylacetylglycine, choline, methyl-indole-3-acetic acid, beta- Alanine, 2-hydroxyisobutyric acid, succinic acid, 4-androstene-3β,17β-diol disulfate 2, 4-hydroxyphenylacetic acid, glycerol, uracil, gulonic acid 1,4-endo Esters, Phenol Sulfate, Dimethylarginine (ADMA + SDMA), Cyclic-gly-pro, Sucrose, Adenosine, Serine, Azelaic Acid (Nazelanedioic Acid), Threonine, Pregnanediol-3- Glucuronide, ethanolamine, gluconic acid, N6-methyladenosine, N-methyl-proline, glycine and glucose 6-phosphate (G6P), 2-hydroxybutyrate and kynurenine. 11. The method of any one of claims 5, 7 and 9, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 7, 11 and/or 13, A subject and a biological sample from the subject are analyzed. 12. A method of measuring bladder cancer stage in a subject with bladder cancer, the method comprising: analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein the one or more biomarkers are selected from Table 5 and/or Table 9, and The level of the one or more biomarkers in the sample is compared to a high-stage bladder cancer and/or low-stage bladder cancer reference level of the one or more biomarkers to determine the stage of the bladder cancer. 13. The method of claim 12, wherein the one or more biomarkers are selected from the group consisting of anserine, pyridoxic acid, adipic acid, xanthuric acid, 1,2-propanediol, phosphorylcholine, acetylcarnitine, 3 -Hydroxybutyric acid (BHBA), palmitoyl sphingomyelin, tyramine, lactic acid, palmitoyl ethanolamide, thromboxane B2, bilirubin (Z,Z), adrenal acid (22:4n6), C-glycosyl tryptophan , Methyl-α-Glucopyranoside, Methyl Phosphoric Acid, 3-Hydroxydecanoic Acid, 3-Hydroxycaprylic Acid, 4-Hydroxyphenylpyruvate, N-Acetyl Threonine, 1-Arachidonyl Glycerol Phosphate Inositol, 5,6-dihydrothymine, 2-hydroxypalmitic acid, coenzyme A, N-acetylserine, nicotinamide adenine dinucleotide (NAD+), docosatrienoic acid (22:3n3 ), reduced glutathione (GSH), prostaglandin A2, glutamine, glutamic acid γ-methyl ester, docosapentaenoic acid (n6 DPA 22:5n6), glycochenodeoxycholic acid , caproylcarnitine, arachidonic acid (20:4n6), pro-hydroxy-pro, docosahexaenoic acid (DHA 22:6n3), laurylcarnitine, succinic acid, adenosine, 2-hydroxy Butyric acid (AHB) and kynurenine. 14. The method of claim 12, wherein a mathematical model is used to determine the stage of bladder cancer in a subject with bladder cancer. 15. A method of aiding in differentiating bladder cancer from prostate cancer in a subject diagnosed with urological cancer, the method comprising analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer and prostate cancer in the sample, wherein the one or more biomarkers are selected from Table 1 and/or Table 11, and The level of the one or more biomarkers in the sample is compared to bladder cancer and prostate cancer reference levels of the one or more biomarkers to distinguish bladder cancer from prostate cancer in the subject. 16. The method of claim 15, wherein a mathematical model is utilized to aid in differentiating bladder cancer from prostate cancer in subjects diagnosed with urological cancer. 17. A method of aiding in differentiating bladder cancer from kidney cancer in a subject diagnosed with urological cancer, the method comprising analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer and kidney cancer in the sample, wherein the one or more biomarkers are selected from Table 1 and/or Table 11, and The level of the one or more biomarkers in the sample is compared to bladder cancer and kidney cancer reference levels of the one or more biomarkers to distinguish bladder cancer from kidney cancer in the subject. 18. The method of claim 17, wherein a mathematical model is utilized to aid in differentiating bladder cancer from kidney cancer in subjects diagnosed with urological cancer. 19. A method of determining or assisting in determining whether a subject is susceptible to bladder cancer, the method comprising: Analyzing a biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein the one or more biomarkers are selected from Tables 1, 5, 7, 9, 11, and/or or 13; and The level of the one or more biomarkers in the sample is compared to bladder cancer positive and/or bladder cancer negative reference levels of the one or more biomarkers to determine whether the subject is susceptible to developing bladder cancer. 20. A method of monitoring the progression/regression of bladder cancer in a subject, the method comprising: Analyzing a first biological sample from the subject to determine the level of one or more biomarkers of bladder cancer in the sample, wherein the one or more biomarkers are selected from Tables 1, 5, 7, 9, 11 and/or 13, and said first sample is obtained from a subject at a first time point; analyzing a second biological sample from the subject to determine the level of the one or more biomarkers, wherein the second sample is obtained from the subject at a second time point; and The level of the one or more biomarkers in the first sample is compared to the level of the one or more biomarkers in the second sample to monitor the progression/regression of bladder cancer in the subject. 21. The method of claim 20, wherein the method further comprises comparing the level of one or more biomarkers in the first sample, the level of one or more biomarkers in the second sample, and/or the first The comparison to the level of the one or more biomarkers in the second sample is compared to a bladder cancer positive and/or bladder cancer negative reference level of the one or more biomarkers. 22. The method of claim 21, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 5, 7, 9, 11 and/or 13, analyzing the subjects and a biological sample from a subject. 23. The method of any preceding claim, wherein determining a BCA score facilitates said method. 24. A method of evaluating the efficacy of a composition for treating bladder cancer, the method comprising: Analyzing a biological sample from a subject having bladder cancer who is currently or previously treated with the composition to determine one or more biomarkers of bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13 level; and The level of the one or more biomarkers in the sample is compared to: (a) the level of the one or more biomarkers in a previously collected biological sample of the subject, wherein prior to treatment with the composition Obtaining said previously collected biological sample from the subject, (b) positive reference level for bladder cancer of one or more biomarkers, and/or (c) negative for bladder cancer of one or more biomarkers reference level. 25. The method of claim 24, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 5, 7, 9, 11 and/or 13, analyzing the subjects and a biological sample from a subject. 26. A method for evaluating the efficacy of a composition in the treatment of bladder cancer, said method comprising: Analyzing a first biological sample of the subject, the first sample at Obtained from a subject at a point in time; administering the composition to a subject; analyzing a second biological sample from the subject to determine the level of one or more biomarkers, the second sample being obtained from the subject at a second time point after administration of the composition; The level of the one or more biomarkers in the first sample is compared to the level of the one or more biomarkers in the second sample to assess the efficacy of the composition for treating bladder cancer. 27. The method of claim 26, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 5, 7, 9, 11 and/or 13, analyzing the subjects and a biological sample from a subject. 28. A method of evaluating the relative efficacy of two or more compositions for the treatment of bladder cancer, said method comprising: Analyzing a first biological sample from a first subject having bladder cancer and currently or previously treated with the first composition to determine one or more selected from Tables 1, 5, 7, 9, 11 and/or 13 levels of biomarkers; analyzing a second biological sample from a second subject with bladder cancer who is currently or previously treated with the second composition to determine the level of one or more biomarkers; and Comparing the level of the one or more biomarkers in the first sample to the level of the one or more biomarkers in the second sample to evaluate the relative effectiveness of the first and second compositions for treating bladder cancer effect. 29. The method of claim 28, wherein the method comprises applying a mathematical model comprising one or more biomarkers or measurements selected from Tables 1, 5, 7, 9, 11 and/or 13, analyzing the subjects and a biological sample from a subject. 30. A method of screening a composition for activity in modulating one or more biomarkers of bladder cancer, the method comprising: contacting the one or more cells with the composition; analyzing at least a portion of the one or more cells or a biological sample associated with the cells to determine the level of one or more biomarkers of bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13; and The level of the one or more biomarkers is compared to a predetermined standard level of the biomarker to determine whether the composition modulates the level of the one or more biomarkers. 31. The method of claim 30, wherein the predetermined standard level of the biomarker is the level of the one or more biomarkers in the one or more cells in the absence of the composition. 32. The method of claim 30, wherein the predetermined standard level of biomarkers is the level of one or more biomarkers in one or more control cells not contacted with the composition. 33. The method of claim 30, wherein said method is performed in vivo. 34. The method of claim 30, wherein said method is performed in vitro. 35. A method for identifying a potential drug target for bladder cancer, the method comprising: identifying one or more biochemical pathways associated with one or more biomarkers of bladder cancer selected from Tables 1, 5, 7, 9, 11 and/or 13; and A protein that affects at least one of the one or more identified biochemical pathways that is a potential drug target for bladder cancer is identified. 36. A method for treating a subject suffering from bladder cancer, said method comprising administering to the subject an effective amount of a compound selected from Tables 1, 5, 7, One or more biomarkers of 9, 11 and/or 13.